Imaging and evaluating embryos, oocytes, and stem cells

ABSTRACT

Methods, compositions and kits for determining the developmental potential of one or more embryos or pluripotent cells and/or the presence of chromosomal abnormalities in one or more embryos or pluripotent cells are provided. These methods, compositions and kits find use in identifying embryos and oocytes in vitro that are most useful in treating infertility in humans.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/227,152, filed Mar. 27, 2014, which is a continuation of U.S.application Ser. No. 12/861,571 filed Aug. 23, 2010, now U.S. Pat. No.8,721,521 issued May 13, 2014 which claims priority to U.S. ProvisionalPatent Application No. 61/332,651, filed May 7, 2010 and U.S.Provisional Patent Application No. 61/236,085, filed Aug. 22, 2009, allof which are herein incorporated by reference in their entireties.

FIELD OF THE INVENTION

This invention relates to the field of biological and clinical testing,and particularly the imaging and evaluation of zygotes/embryos, oocytes,and stem cells from both humans and animals.

BACKGROUND OF THE INVENTION

Infertility is a common health problem that affects 10-15% of couples ofreproductive-age. In the United States alone in the year 2006,approximately 140,000 cycles of in vitro fertilization (IVF) wereperformed (cdc.gov/art). This resulted in the culture of more than amillion embryos annually with variable, and often ill-defined, potentialfor implantation and development to term. The live birth rate, percycle, following IVF was just 29%, while on average 30% of live birthsresulted in multiple gestations (cdc.gov/art). Multiple gestations havewell-documented adverse outcomes for both the mother and fetuses, suchas miscarriage, pre-term birth, and low birth rate. Potential causes forfailure of IVF are diverse; however, since the introduction of IVF in1978, one of the major challenges has been to identify the embryos thatare most suitable for transfer and most likely to result in termpregnancy.

The understanding in the art of basic embryo development is limited asstudies on human embryo biology remain challenging and often exempt fromresearch funding. Consequently, most of the current knowledge of embryodevelopment derives from studies of model organisms. However, whileembryos from different species go through similar developmental stages,the timing varies by species. These differences, and many others make itinappropriate to directly extrapolate from one species to another.(Taft, R. E. (2008) Theriogenology 69(1): 10-16). The general pathwaysof human development, as well as the fundamental underlying moleculardeterminants, are unique to human embryo development. For example, inmice, embryonic transcription is activated approximately 12 hourspost-fertilization, concurrent with the first cleavage division, whereasin humans embryonic gene activation (EGA) occurs on day 3, around the8-cell stage (Bell, C. E., et al. (2008) Mol. Hum. Reprod. 14:691-701;Braude, P., et al. (1988) Nature 332:459-461; Hamatani, T. et al. (2004)Proc. Natl. Acad. Sci. 101:10326-10331; Dobson, T. et al. (2004) HumanMolecular Genetics 13(14): 1461-1470). In addition, the genes that aremodulated in early human development are unique (Dobson, T. et al.(2004) Human Molecular Genetics 13(14): 1461-1470). Moreover, in otherspecies such as the mouse, more than 85% of embryos cultured in vitroreach the blastocyst stage, one of the first major landmarks inmammalian development, whereas cultured human embryos have an averageblastocyst formation rate of approximately 30-50%, with a high incidenceof mosaicism and aberrant phenotypes, such as fragmentation anddevelopmental arrest (Rienzi, L. et al. (2005) Reprod. Biomed. Online10:669-681; Alikani, M., et al. (2005) Mol. Hum. Reprod. 11:335-344;Keltz, M. D., et al. (2006) Fertil. Steril. 86:321-324; French, D. B.,et al. (2009) Fertil. Steril.). In spite of such differences, themajority of studies of preimplantation embryo development derive frommodel organisms and are difficult to relate to human embryo development(Zernicka-Goetz, M. (2002) Development 129:815-829; Wang, Q., et al.(2004) Dev Cell. 6:133-144; Bell, C. E., et al. (2008) Mol. Hum. Reprod.14:691-701; Zernicka-Goetz, M. (2006) Curr. Opin. Genet. Dev.16:406-412; Mtango, N. R., et al. (2008) Int. Rev. Cell. Mol. Biol.268:223-290).

Traditionally in IVF clinics, human embryo viability has been assessedby simple morphologic observations such as the presence ofuniformly-sized, mononucleate blastomeres and the degree of cellularfragmentation (Rijinders P M, Jansen C A M. (1998) Hum Reprod13:2869-73; Milki A A, et al. (2002) Fertil Steril 77:1191-5). Morerecently, additional methods such as extended culture of embryos (to theblastocyst stage at day 5) and analysis of chromosomal status viapreimplantation genetic diagnosis (PGD) have also been used to assessembryo quality (Milki A, et al. (2000) Fertil Steril 73:126-9; FragouliE, (2009) Fertil Steril June 21 [EPub ahead of print]; El-Toukhy T, etal. (2009) Hum Reprod 6:20; Vanneste E, et al. (2009) Nat Med15:577-83). However, potential risks of these methods also exist in thatthey prolong the culture period and disrupt embryo integrity(Manipalviratn S, et al. (2009) Fertil Steril 91:305-15; Mastenbroek S,et al. (2007) N Engl J Med. 357:9-17).

Recently it has been shown that time-lapse imaging can be a useful toolto observe early embryo development. Some methods have used time-lapseimaging to monitor human embryo development following intracytoplasmicsperm injection (ICSI) (Nagy et al. (1994) Human Reproduction. 9(9):1743-1748; Payne et al. (1997) Human Reproduction. 12:532-541). Polarbody extrusion and pro-nuclear formation were analyzed and correlatedwith good morphology on day 3. However, no parameters were correlatedwith blastocyst formation or pregnancy outcomes. Other methods havelooked at the onset of first cleavage as an indicator to predict theviability of human embryos (Fenwick, et al. (2002) Human Reproduction,17:407-412; Lundin, et al. (2001) Human Reproduction 16:2652-2657).However, these methods do not recognize the importance of the durationof cytokinesis or time intervals between early divisions.

Other methods have used time-lapse imaging to measure the timing andextent of cell divisions during early embryo development(WO/2007/144001). However, these methods disclose only a basic andgeneral method for time-lapse imaging of bovine embryos, which aresubstantially different from human embryos in terms of developmentalpotential, morphological behavior, molecular and epigenetic programs,and timing and parameters surrounding transfer. For example, bovineembryos take substantially longer to implant compared to human embryos(30 days and 9 days, respectively). (Taft, (2008) Theriogenology 69(1):10-16. Moreover, no specific imaging parameters or time intervals aredisclosed that might be predictive of human embryo viability.

More recently, time-lapse imaging has been used to observe human embryodevelopment during the first 24 hours following fertilization (Lemmen etal. (2008) Reproductive BioMedicine Online 17(3):385-391). The synchronyof nuclei after the first division was found to correlate with pregnancyoutcomes. However, this work concluded that early first cleavage was notan important predictive parameter, which contradicts previous studies(Fenwick, et al. (2002) Human Reproduction 17:407-412; Lundin, et al.(2001) Human Reproduction 16:2652-2657).

Finally, no studies have validated the imaging parameters throughcorrelation with the molecular programs or chromosomal composition ofthe embryos. Methods of human embryo evaluation are thus lacking inseveral respects and can be improved by the present methods, whichinvolve novel applications of time-lapse microscopy, image analysis, andcorrelation of the imaging parameters with molecular profiles andchromosomal composition. The present invention addresses these issues.

SUMMARY OF THE INVENTION

Methods, compositions and kits for determining the developmentalpotential of one or more embryos or pluripotent cells in one or moreembryos or pluripotent cells are provided. These methods, compositionsand kits find use in identifying embryos and oocytes in vitro that havea good developmental potential, i.e. the ability or capacity to developinto a blastocyst, which are thus useful in methods of treatinginfertility in humans, and the like.

In some aspects of the invention, methods are provided for determiningthe developmental potential of an embryo or a pluripotent cell. In suchaspects, one or more cellular parameters of an embryo or pluripotentcell is measured to arrive at a cell parameter measurement. The cellparameter is then employed to provide a determination of thedevelopmental potential of the embryo or pluripotent cell, whichdetermination may be used to guide a clinical course of action. In someembodiments, the cell parameter is a morphological event that ismeasurable by time-lapse microscopy. In some embodiments, e.g. when anembryo is assayed, the one or more cell parameters is: the duration of acytokinesis event, e.g. cytokinesis 1; the time interval betweencytokinesis 1 and cytokinesis 2; and the time interval betweencytokinesis 2 and cytokinesis 3. In certain embodiments, the duration ofcell cycle 1 is also utilized as a cell parameter. In some embodiments,the cell parameter measurement is employed by comparing it to acomparable cell parameter measurement from a reference embryo, and usingthe result of this comparison to provide a determination of thedevelopmental potential of the embryo. In some embodiments, the embryois a human embryo. In some embodiments, the cell parameter is a geneexpression level that is measured to arrive at a gene expressionmeasurement. In some embodiments, the gene expression measurement isemployed by comparing it to a gene expression measurement from areference pluripotent cell or embryo or one or more cells therefrom,where result of this comparison is employed to provide a determinationof the developmental potential of the pluripotent cell or embryo. Insome embodiments, the embryo is a human embryo.

In some aspects of the invention, methods are provided for rankingembryos or pluripotent cells for their developmental potential relativeto the other embryos or pluripotent cells in the group. In suchembodiments, one or more cellular parameters of the embryos orpluripotent cells in the group is measured to arrive at a cell parametermeasurement for each of the embryos or pluripotent cells. The cellparameter measurements are then employed to determine the developmentalpotential of each of the embryos or pluripotent cells in the grouprelative to one another, which determination may be used to guide aclinical course of action. In some embodiments, the cell parameter is amorphological event that is measurable by time-lapse microscopy. In someembodiments, e.g. when embryos are ranked, the one or more cellparameters are the duration of a cytokinesis event, e.g. cytokinesis 1;the time interval between cytokinesis 1 and cytokinesis 2; and the timeinterval between cytokinesis 2 and cytokinesis 3. In certainembodiments, the duration of cell cycle 1 is also measured. In someembodiments, the cell parameter is the expression level of one or moregenes. In some embodiments, the one or more cell parameter measurementsare employed by comparing the cell parameter measurements from each ofthe embryos or pluripotent cells in the group to one another todetermine the developmental potential of the embryos or pluripotentcells relative to one another. In some embodiments, the one or more cellparameter measurements are employed by comparing each cell parametermeasurement to a cell parameter measurement from a reference embryo orpluripotent cell to determine the developmental potential for eachembryo or pluripotent cell, and comparing those developmental potentialsto determine the developmental potential of the embryos or pluripotentcells relative to one another.

In some aspects of the invention, methods are provided for providingembryos with good developmental potential for transfer to a female forassisted reproduction (IVF). In such aspects, one or more embryos iscultured under conditions sufficient for embryo development. One or morecellular parameters is then measured in the one or more embryos toarrive at a cell parameter measurement. The cell parameter measurementis then employed to provide a determination of the developmentalpotential of the one or more embryos. The one or more embryos thatdemonstrate good developmental potential is then transferred into afemale.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is best understood from the following detailed descriptionwhen read in conjunction with the accompanying drawings. It isemphasized that, according to common practice, the various features ofthe drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.Included in the drawings are the following figures.

FIG. 1 is a flow chart showing processes used to evaluate embryos.

FIG. 2 is a series of photographs showing cell cleavage and divisionover a period of 6 days. Images are labeled day 1 through day 6. Scalebar represents 50 μm.

FIG. 3 is a bar graph showing percentages of successful development intoblastocysts from 1-cell embryos (zygotes). Over the course of 4 separateexperiments, a total of 100 embryos were observed until Day 5 to 6 viatime-lapse microscopy. The percentage of cells reaching each indicatedstage (blastocyst, 8-cell, 4- to 7-cell, 2- to 3-cell and 1-cell) isshown.

FIG. 4 is a series of four different embryos being followed for thetimes indicated.

FIG. 5 is a diagram showing time lapses between stages used for thepresent evaluations, including the duration of the first cytokinesis,time between the first and second division (measured as the timeinterval between the resolution of cytokinesis 1 and the onset ofcytokinesis 2), and time between the 2nd and 3rd mitosis (measured asthe time interval between the initiation of cytokinesis 2 and theinitiation of cytokinesis 3).

FIG. 6 is a 3-D point graph showing the measurement of three events,including the duration of the first cytokinesis, the time intervalbetween the first and second cell divisions (measured as the timeinterval between the resolution of cytokinesis 1 and the onset ofcytokinesis 2), and the time interval between the second cell and thirdcell divisions (measured as the time interval between the initiation ofcytokinesis 2 and the initiation of cytokinesis 3), for a large group ofembryos. The embryos that reach the blastocyst stage (marked withcircles) are shown to cluster together on the 3-D graph, while embryosthat arrest (marked with X's) before reaching blastocyst are scatteredthroughout.

FIG. 7 is a graph showing a receiver operating characteristic (ROC)curve for predicting blastocyst formation using the 3 dynamicmorphological parameters.

FIG. 8 is a radar graph showing gene expression levels of 52 genes from6 arrested 1- to 2-cell embryos and 5 normal 1- to 2-cell embryos. Thedifference in expression levels between normal and abnormal embryos wasstatistically significant for those genes highlighted in yellow anddenoted with an asterisk, as determined by the Mann-Whitney test.

FIG. 9 is a bar graph showing expression levels of different genes in anarrested 2-cell embryo and normal 2-cell embryos. A select number of thetime-lapse images for the arrested 2-cell embryo are shown at the top.

FIG. 10 is a bar graph showing a comparison of the same genes presentedin FIG. 9, in an arrested 4-cell embryo and normal 4-cell embryos. Aselect number of the time-lapse images for the arrested 4-cell embryoare shown at the top.

FIG. 11 is a series of bar graphs showing gene expression patterns(ESSP) having 4 distinct patterns. Indicated are times of early transferprior to embryonic gene activation (day 2) and typical expression at day3.

FIG. 12 shows gene expression of genes from single blastomeres atdifferent stages. (A) Gene expression of two genes, CTNNB1 and CDX2 fromsingle blastomeres plotted at different cell stages and showing changesin these gene expression levels at different stages, e.g. 2 cells, 3cells, morula and blastocyst. (B) Gene expression signatures in barsrepresenting genes expressed in the maternal program as compared togenes expressed from the zygotic program.

FIG. 13 is a drawing of a model for using time-lapse image analysis andcorrelated molecular analysis to assess embryo viability.

FIG. 14 is a series of photographs showing three stages of developmentduring in vitro oocyte maturation.

FIG. 15 is a series of photographs showing the process of embryodevelopment after in vitro oocyte maturation.

FIG. 16 is a flow chart showing processes used to assess oocytes.

FIG. 17 is a flow chart showing processes used to assess stem cells andpluripotent stem cells.

FIG. 18 is a series of photographs showing the process of inducedpluripotent stem cells differentiating into neuron rosettes.

FIG. 19 is a table of the categories into which the genes assayed forexpression level may be categorized, including the number of genes percategory.

FIG. 20 is a table of the four Embryonic Stage Specific Patterns (ESSPs)that were identified during gene expression analysis of 141 normallydeveloped single embryos and single blastomeres, and the categorizationof the genes into each one of the these categories.

FIG. 21 shows automated image analysis demonstrating the ability ofimaging parameters to predict blastocyst formation. (A) Shows theresults of the tracking algorithm for a single embryo. (B) Shows a setof 14 embryos that were analyzed. (C) Shows the comparison of manualimage analysis to automated image analysis for the duration ofcytokinesis. (D) Shows the comparison of manual image analysis for thetime between first and second mitosis. (E) Shows the comparison of goodblastocyst morphology to bad blastocyst morphology.

FIG. 22 is a schematic drawing of a dark field microscope according tothe present invention; the inset on the left shows a laser machineddarkfield patch set up.

FIG. 23 is a schematic drawing of an array of three microscopes asillustrated in FIG. 22, mounted on a support for installation into anincubator and for computer connections. FIG. 23A shows the microscopes,and FIG. 23B shows the microscopes inside and incubator.

FIG. 24 is a screen shot of image capture software used in the presentwork, showing embryos being imaged from 3 channels.

FIG. 25 A through D is a series of four photographs showing selectedtime-lapse image from experiment 2, station 2. FIGS. 25A and 25B areimages captured before media change, and FIGS. 25C and 25D are imagescaptured after media change.

FIG. 26 A through D is a series of four photographs showing selectedtime-lapse images from experiment 1, station 2. FIGS. 26A and 26B areimages captured before media change, and FIGS. 26C and 26D are imagescaptured after media change.

FIGS. 27 A and B are drawings of a custom petri dish with micro-wells.FIG. 27A shows a drawing of the dish with dimensions, and FIG. 27B showsa 3D-view of the micro-wells.

FIGS. 28 A and B are graphs showing cell activity with and without priorimage registration. FIGS. 28A and 28B together show that registrationcleans up the results and removes spikes due to embryo shifting orrotating.

FIGS. 29 A and B are graphs (left) and cell photographs (right) showingcell activity for normal and abnormal embryos. Together, FIG. 29A andFIG. 29B show that, at day 3, the embryos have similar morphology, buttheir cell activity plots are drastically different and only one of themdevelops into a blastocyst.

FIG. 30 is a graph showing the difference in pixel intensities betweensuccessive pairs of images during embryo development. This can be usedon its own to assess embryo viability, or as a way to improve otheralgorithms, such as a particle filter, by determining how many particles(predicted embryo models) should be used.

FIG. 31 A-G is a series of seven photographs showing results from 2Dtracking at various cell stages. Cells progress as indicated by theframe numbers associated with each picture pair: Frame 15 (FIG. 31A), 45(B), 48 (C), 189 (D), 190 (E), 196 (F) and 234 (G). The bottom row showsthe overlaid simulated images. The contours are visible cell membranes,and the dotted white lines are occluded membranes. Image frames arecaptured every 5 minutes, and only a few are displayed.

FIGS. 32 A and B is a series of photographs and drawings showing twosuccessful cases of 3D cell tracking. The illustrations under each photoof an embryo show the top-down view of the 3D model, except for frame314 and frame 228, which show side-views of the models in frame 314 andframe 228, respectively. The image frames were captured every 5 minutes.

FIG. 33 is a diagrammatic representation of particle filter results fora 1-cell to 2-cell division. The data points are the 3D location of thecell centers. Dots are shown for 1-cell models, 2-cell models, 3-cellmodels, and 4-cell models. The top row shows the particles afterprediction, and the bottom row shows particles after re-sampling.

FIGS. 34 A and B are graphs showing a comparison of automated vs. manualimage analysis for a set of 14 embryos. FIG. 34A shows the comparisonfor the duration of first cytokineis, and FIG. 34B shows the comparisonfor the time between 1^(st) and 2^(nd) mitosis.

FIG. 35 is a flow chart showing how image analysis is used to modelembryos and measure certain morphological parameters.

DETAILED DESCRIPTION OF THE INVENTION

Before the present methods and compositions are described, it is to beunderstood that this invention is not limited to particular method orcomposition described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present invention will be limited onlyby the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimits of that range is also specifically disclosed. Each smaller rangebetween any stated value or intervening value in a stated range and anyother stated or intervening value in that stated range is encompassedwithin the invention. The upper and lower limits of these smaller rangesmay independently be included or excluded in the range, and each rangewhere either, neither or both limits are included in the smaller rangesis also encompassed within the invention, subject to any specificallyexcluded limit in the stated range. Where the stated range includes oneor both of the limits, ranges excluding either or both of those includedlimits are also included in the invention.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, some potential andpreferred methods and materials are now described. All publicationsmentioned herein are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited. It is understood that the present disclosuresupercedes any disclosure of an incorporated publication to the extentthere is a contradiction.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “acell” includes a plurality of such cells and reference to “the peptide”includes reference to one or more peptides and equivalents thereof, e.g.polypeptides, known to those skilled in the art, and so forth.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present invention isnot entitled to antedate such publication by virtue of prior invention.Further, the dates of publication provided may be different from theactual publication dates which may need to be independently confirmed.

DEFINITIONS

Methods, compositions and kits for determining the developmentalpotential of one or more embryos or pluripotent cells and/or thepresence of chromosomal abnormalities in one or more embryos orpluripotent cells are provided. These methods, compositions and kitsfind use in identifying embryos and oocytes in vitro that are mostuseful in treating infertility in humans. These and other objects,advantages, and features of the invention will become apparent to thosepersons skilled in the art upon reading the details of the subjectmethods and compositions as more fully described below.

The terms “developmental potential’ and “developmental competence’ areused herein to refer to the ability or capacity of a healthy embryo orpluripotent cell to grow or develop.

The term “embryo’ is used herein to refer both to the zygote that isformed when two haploid gametic cells, e.g. an unfertilized secondaryoocyte and a sperm cell, unite to form a diploid totipotent cell, e.g. afertilized ovum, and to the embryo that results from the immediatelysubsequent cell divisions, i.e. embryonic cleavage, up through themorula, i.e. 16-cell stage and the blastocyst stage (with differentiatedtrophoectoderm and inner cell mass).

The term “pluripotent cell’ is used herein to mean any cell that has theability to differentiate into multiple types of cells in an organism.Examples of pluripotent cells include stem cells oocytes, and 1-cellembryos (i.e. zygotes).

The term “stem cell’ is used herein to refer to a cell or a populationof cells which: (a) has the ability to self-renew, and (b) has thepotential to give rise to diverse differentiated cell types. Frequently,a stem cell has the potential to give rise to multiple lineages ofcells. As used herein, a stem cell may be a totipotent stem cell, e.g. afertilized oocyte, which gives rise to all of the embryonic andextraembryonic tissues of an organism; a pluripotent stem cell, e.g. anembryonic stem (ES) cell, embryonic germ (EG) cell, or an inducedpluripotent stem (iPS) cell, which gives rise to all of embryonictissues of an organism, i.e. endoderm, mesoderm, and ectoderm lineages;a multipotent stem cell, e.g. a mesenchymal stem cell, which gives riseto at least two of the embryonic tissues of an organism, i.e. at leasttwo of endoderm, mesoderm and ectoderm lineages, or it may be atissue-specific stem cell, which gives rise to multiple types ofdifferentiated cells of a particular tissue. Tissue-specific stem cellsinclude tissue-specific embryonic cells, which give rise to the cells ofa particular tissue, and somatic stem cells, which reside in adulttissues and can give rise to the cells of that tissue, e.g. neural stemcells, which give rise to all of the cells of the central nervoussystem, satellite cells, which give rise to skeletal muscle, andhematopoietic stem cells, which give rise to all of the cells of thehematopoietic system.

The term “oocyte” is used herein to refer to an unfertilized female germcell, or gamete. Oocytes of the subject application may be primaryoocytes, in which case they are positioned to go through or are goingthrough meiosis I, or secondary oocytes, in which case they arepositioned to go through or are going through meiosis II.

By “meiosis” it is meant the cell cycle events that result in theproduction of gametes. In the first meiotic cell cycle, or meiosis I, acell's chromosomes are duplicated and partitioned into two daughtercells. These daughter cells then divide in a second meiotic cell cycle,or meiosis II, that is not accompanied by DNA synthesis, resulting ingametes with a haploid number of chromosomes.

By the “germinal vesicle” stage it is meant the stage of a primaryoocyte's maturation that correlates with prophase I of the meiosis Icell cycle, i.e. prior to the first division of the nuclear material.Oocytes in this stage are also called “germinal vesicle oocytes”, forthe characteristically large nucleus, called a germinal vesicle. In anormal human oocyte cultured in vitro, germinal vesicle occurs about6-24 hours after the start of maturation.

By the “metaphase I” stage it is meant the stage of a primary ooctye'smaturation that correlates with metaphase I of the meiosis I cell cycle.In comparison to germinal vesicle oocytes, metaphase I oocytes do nothave a large, clearly defined nucleus. In a normal human oocyte culturedin vitro, metaphase I occurs about 12-36 hours after the start ofmaturation.

By the “metaphase II” stage it is meant the stage of a secondaryooctye's maturation that correlates with metaphase II of the meiosis IIcell cycle. Metaphase II is distinguishable by the extrusion of thefirst polar body. In a normal human oocyte cultured in vitro, metaphaseII occurs about 24-48 hours after the start of maturation

By a “mitotic cell cycle”, it is meant the events in a cell that resultin the duplication of a cell's chromosomes and the division of thosechromosomes and a cell's cytoplasmic matter into two daughter cells. Themitotic cell cycle is divided into two phases: interphase and mitosis.In interphase, the cell grows and replicates its DNA. In mitosis, thecell initiates and completes cell division, first partitioning itsnuclear material, and then dividing its cytoplasmic material and itspartitioned nuclear material (cytokinesis) into two separate cells.

By a “first mitotic cell cycle” or “cell cycle 1” it is meant the timeinterval from fertilization to the completion of the first cytokinesisevent, i.e. the division of the fertilized oocyte into two daughtercells. In instances in which oocytes are fertilized in vitro, the timeinterval between the injection of human chorionic gonadotropin (HCG)(usually administered prior to oocyte retrieval) to the completion ofthe first cytokinesis event may be used as a surrogate time interval.

By a “second mitotic cell cycle” or “cell cycle 2” it is meant thesecond cell cycle event observed in an embryo, the time interval betweenthe production of daughter cells from a fertilized oocyte by mitosis andthe production of a first set of granddaughter cells from one of thosedaughter cells (the “leading daughter cell”, or daughter cell A) bymitosis. Upon completion of cell cycle 2, the embryo consists of 3cells. In other words, cell cycle 2 can be visually identified as thetime between the embryo containing 2-cells and the embryo containing3-cells.

By a “third mitotic cell cycle” or “cell cycle 3” it is meant the thirdcell cycle event observed in an embryo, typically the time interval fromthe production of daughter cells from a fertilized oocyte by mitosis andthe production of a second set of granddaughter cells from the seconddaughter cell (the “lagging daughter cell” or daughter cell B) bymitosis. Upon completion of cell cycle 3, the embryo consists of 4cells. In other words, cell cycle 3 can be visually identified as thetime between the embryo containing 3-cells and the embryo containing4-cells.

By “first cleavage event”, it is meant the first division, i.e. thedivision of the oocyte into two daughter cells, i.e. cell cycle 1. Uponcompletion of the first cleavage event, the embryo consists of 2 cells.

By “second cleavage event”, it is meant the second set of divisions,i.e. the division of leading daughter cell into two granddaughter cellsand the division of the lagging daughter cell into two granddaughtercells. In other words, the second cleavage event consists of both cellcycle 2 and cell cycle 3. Upon completion of second cleavage, the embryoconsists of 4 cells.

By “third cleavage event”, it is meant the third set of divisions, i.e.the divisions of all of the granddaughter cells. Upon completion of thethird cleavage event, the embryo typically consists of 8 cells.

By “cytokinesis” or “cell division” it is meant that phase of mitosis inwhich a cell undergoes cell division. In other words, it is the stage ofmitosis in which a cell's partitioned nuclear material and itscytoplasmic material are divided to produce two daughter cells. Theperiod of cytokinesis is identifiable as the period, or window, of timebetween when a constriction of the cell membrane (a “cleavage furrow”)is first observed and the resolution of that constriction event, i.e.the generation of two daughter cells. The initiation of the cleavagefurrow may be visually identified as the point in which the curvature ofthe cell membrane changes from convex (rounded outward) to concave(curved inward with a dent or indentation). This is illustrated in FIG.4 top panel by white arrows pointing at 2 cleavage furrows. The onset ofcell elongation may also be used to mark the onset of cytokinesis, inwhich case the period of cytokinesis is defined as the period of timebetween the onset of cell elongation and the resolution of the celldivision.

By “first cytokinesis” or “cytokinesis 1” it is meant the first celldivision event after fertilization, i.e. the division of a fertilizedoocyte to produce two daughter cells. First cytokinesis usually occursabout one day after fertilization.

By “second cytokinesis” or “cytokinesis 2”, it is meant the second celldivision event observed in an embryo, i.e. the division of a daughtercell of the fertilized oocyte (the “leading daughter cell”, or daughterA) into a first set of two granddaughters.

By “third cytokinesis” or “cytokinesis 3”, it is meant the third celldivision event observed in an embryo, i.e. the division of the otherdaughter of the fertilized oocyte (the “lagging daughter cell”, ordaughter B) into a second set of two granddaughters.

The term “fiduciary marker” or “fiducial marker,” is an object used inthe field of view of an imaging system which appears in the imageproduced, for use as a point of reference or a measure. It may be eithersomething placed into or on the imaging subject, or a mark or set ofmarks in the reticle of an optical instrument.

The term “micro-well” refers to a container that is sized on a cellularscale, preferably to provide for accommodating a single eukaryotic cell.

Pluripotent Cells and Embryos of Interest

In methods of the invention, one or more embryos or pluripotent cells isassessed for its developmental potential by measuring one or morecellular parameters of the embryo(s) or pluripotent cell(s) andemploying these measurements to determine the developmental potential ofthe embryo(s) or pluripotent cell(s). The information thus derived maybe used to guide clinical decisions, e.g. whether or not to transfer anin vitro fertilized embryo, whether or not to transplant a cultured cellor cells.

Examples of embryos that may be assessed by the methods of the inventioninclude 1-cell embryos (also referred to as zygotes), 2-cell embryos,3-cell embryos, 4-cell embryos, 5-cell embryos, 6-cell embryos, 8-cellembryos, etc. typically up to and including 16-cell embryos, any ofwhich may be derived by any convenient manner, e.g. from an oocyte thathas matured in vivo or from an oocyte that has matured in vitro.

Examples of pluripotent cells that may be assessed by the methods of theinvention include totipotent stem cells, e.g. oocytes, such as primaryoocytes and secondary oocytes; pluripotent stem cells, e.g. ES cells, EGcells, iPS cells, and the like; multipotent cells, e.g. mesenchymal stemcells; and tissue-specific stem cells. They may be from any stage oflife, e.g. embryonic, neonatal, a juvenile or adult, and of either sex,i.e. XX or XY.

Embryos and pluripotent cells may be derived from any organism, e.g. anymammalian species, e.g. human, primate, equine, bovine, porcine, canine,feline, etc. Preferable, they are derived from a human. They may bepreviously frozen, e.g. embryos cryopreserved at the 1-cell stage andthen thawed, or frozen and thawed oocytes and stem cells. Alternatively,they may be freshly prepared, e.g., embryos that are freshly preparedfrom oocytes by in vitro fertilization techniques; oocytes that arefreshly harvested and/or freshly matured through in vitro maturationtechniques or that are derived from pluripotent stem cellsdifferentiated in vitro into germ cells and matured into oocytes; stemcells freshly prepared from the dissociation and culturing of tissues bymethods known in the art; and the like. They may be cultured under anyconvenient conditions known in the art to promote survival, growth,and/or development of the sample to be assessed, e.g. for embryos, underconditions such as those used in the art of in vitro fertilization; see,e.g., U.S. Pat. No. 6,610,543, U.S. Pat. No. 6,130,086, U.S. Pat. No.5,837,543, the disclosures of which are incorporated herein byreference; for oocytes, under conditions such as those used in the artto promote oocyte maturation; see, e.g., U.S. Pat. No. 5,882,928 andU.S. Pat. No. 6,281,013, the disclosures of which are incorporatedherein by reference; for stem cells under conditions such as those usedin the art to promote proliferation, see, e.g. U.S. Pat. No. 6,777,233,U.S. Pat. No. 7,037,892, U.S. Pat. No. 7,029,913, U.S. Pat. No.5,843,780, and U.S. Pat. No. 6,200,806, US Application No. 2009/0047263;US Application No. 2009/0068742, the disclosures of which areincorporated herein by reference. Often, the embryos/pluripotent cellsare cultured in a commercially available medium such as KnockOut DMEM,DMEM-F12, or Iscoves Modified Dulbecco's Medium that has beensupplemented with serum or serum substitute, amino acids, and growthfactors tailored to the needs of the particular embryo/pluripotent cellbeing assessed.

Time-Lapse Imaging Analysis

In some embodiments, the embryos/pluripotent cells are assessed bymeasuring cell parameters by time-lapse imaging. The embryos/pluripotentcells may be cultured in standard culture dishes. Alternatively, theembryos/pluripotent cells may be cultured in custom culture dishes, e.g.custom culture dishes with optical quality micro-wells as describedherein. In such custom culture dishes, each micro-well holds a singleembryo/pluripotent cell, and the bottom surface of each micro-well hasan optical quality finish such that the entire group of embryos within asingle dish can be imaged simultaneously by a single miniaturemicroscope with sufficient resolution to follow the cell mitosisprocesses. The entire group of micro-wells shares the same media drop inthe culture dish, and can also include an outer wall positioned aroundthe micro-wells for stabilizing the media drop, as well as fiducialmarkers placed near the micro-wells. The hydrophobicity of the surfacecan be adjusted with plasma etching or another treatment to preventbubbles from forming in the micro-wells when filled with media.Regardless of whether a standard culture dish or a custom culture dishis utilized, during culture, one or more developing embryos may becultured in the same culture medium, e.g. between 1 and 30 embryos maybe cultured per dish.

Images are acquired over time, and are then analyzed to arrive atmeasurements of the one or more cellular parameters. Time-lapse imagingmay be performed with any computer-controlled microscope that isequipped for digital image storage and analysis, for example, invertedmicroscopes equipped with heated stages and incubation chambers, orcustom built miniature microscope arrays that fit inside a conventionalincubator. The array of miniature microscopes enables the concurrentculture of multiple dishes of samples in the same incubator, and isscalable to accommodate multiple channels with no limitations on theminimum time interval between successive image capture. Using multiplemicroscopes eliminates the need to move the sample, which improves thesystem accuracy and overall system reliability. The individualmicroscopes in the incubator can be partially or fully isolated,providing each culture dish with its own controlled environment. Thisallows dishes to be transferred to and from the imaging stations withoutdisturbing the environment of the other samples.

The imaging system for time-lapse imaging may employ brightfieldillumination, darkfield illumination, phase contrast, Hoffman modulationcontrast, differential interference contrast, or fluorescence. In someembodiments, darkfield illumination may be used to provide enhancedimage contrast for subsequent feature extraction and image analysis. Inaddition, red or near-infrared light sources may be used to reducephototoxicity and improve the contrast ratio between cell membranes andthe inner portion of the cells.

Images that are acquired may be stored either on a continuous basis, asin live video, or on an intermittent basis, as in time lapsephotography, where a subject is repeatedly imaged in a still picture.Preferably, the time interval between images should be between 1 to 30minutes in order to capture significant morphological events asdescribed below. In an alternative embodiment, the time interval betweenimages could be varied depending on the amount of cell activity. Forexample, during active periods images could be taken as often as everyfew seconds or every minute, while during inactive periods images couldbe taken every 10 or 15 minutes or longer. Real-time image analysis onthe captured images could be used to detect when and how to vary thetime intervals. In our methods, the total amount of light received bythe samples is estimated to be equivalent to approximately 24 minutes ofcontinuous low-level light exposure for 5-days of imaging. The lightintensity for a time-lapse imaging systems is significantly lower thanthe light intensity typically used on an assisted reproductionmicroscope due to the low-power of the LEDs (for example, using a 1W LEDcompared to a typical 100W Halogen bulb) and high sensitivity of thecamera sensor. Thus, the total amount of light energy received by anembryo using the time-lapse imaging system is comparable to or less thanthe amount of energy received during routine handling at an IVF clinic.In addition, exposure time can be significantly shortened to reduce thetotal amount of light exposure to the embryo/pluripotent cell. For2-days of imaging, with images captured every 5 minutes at 0.5 secondsof light exposure per image, the total amount of low-level lightexposure is less than 5 minutes.

Following image acquisition, the images are extracted and analyzed fordifferent cellular parameters, for example, cell size, thickness of thezona pellucida, degree of fragmentation, symmetry of daughter cellsresulting from a cell division, time intervals between the first fewmitoses, and duration of cytokinesis.

Cell parameters that may be measured by time-lapse imaging are usuallymorphological events. For example, in assessing embryos, time-lapseimaging may be used to measure the duration of a cytokinesis event, e.g.cytokinesis 1, cytokinesis 2, cytokinesis 3, or cytokinesis 4, where theduration of a cytokinesis event is defined as the time interval betweenthe first observation of a cleavage furrow (the initiation ofcytokinesis) and the resolution of the cleavage furrow into two daughtercells (i.e. the production of two daughter cells). Another parameter ofinterest is the duration of a cell cycle event, e.g. cell cycle 1, cellcycle 2, cell cycle 3, or cell cycle 4, where the duration of a cellcycle event is defined as the time interval between the production of acell (for cell cycle 1, the fertilization of an ovum; for later cellcycles, at the resolution of cytokinesis) and the production of twodaughter cells from that cell. Other cell parameters of interest thatcan be measured by time-lapse imaging include time intervals that aredefined by these cellular events, e.g. (a) the time interval betweencytokinesis 1 and cytokinesis 2, definable as any one of the intervalbetween initiation of cytokinesis 1 and the initiation of cytokinesis 2,the interval between the resolution of cytokinesis 1 and the resolutionof cytokinesis 2, the interval between the initiation of cytokinesis 1and the resolution of cytokinesis 2; or the interval between theresolution of cytokinesis 1 and the initiation of cytokinesis 2; or (b)the time interval between cytokinesis 2 and cytokinesis 3, definable asany one of the interval between the initiation of cytokinesis 2 and theinitiation of cytokinesis 3, or the interval between resolution of thecytokinesis 2 and the resolution of cytokinesis 3, or the intervalbetween initiation of cytokinesis 2 and the resolution of cytokinesis 3,or the interval between resolution of cytokinesis 2 and the initiationof cytokinesis 3.

For the purposes of in vitro fertilization, it is consideredadvantageous that the embryo be transferred to the uterus early indevelopment, e.g. by day 2 or day 3, i.e. up through the 8-cell stage,to reduce embryo loss due to disadvantages of culture conditionsrelative to the in vitro environment, and to reduce potential adverseoutcomes associated with epigenetic errors that may occur duringculturing (Katari et al. (2009) Hum Mol Genet. 18(20):3769-78; Sepúlvedaet al. (2009) Fertil Steril. 91(5): 1765-70). Accordingly, it ispreferable that the measurement of cellular parameters take place within2 days of fertilization, although longer periods of analysis, e.g. about36 hours, about 54 hours, about 60 hours, about 72 hours, about 84hours, about 96 hours, or more, are also contemplated by the presentmethods.

Examples of cell parameters in a maturing oocyte that may be assessed bytime-lapse imaging include, without limitation, changes in morphology ofthe oocyte membrane, e.g. the rate and extent of separation from thezona pellucida; changes in the morphology of the oocyte nucleus, e.g.the initiation, completion, and rate of germinal vesicle breakdown(GVBD); the rate and direction of movement of granules in the cytoplasmand nucleus; the cytokinesis of oocyte and first polar body and themovement of and/or duration of the extrusion of the first polar body.Other parameters include the duration of cytokinesis of the maturesecondary oocyte and the second polar body.

Examples of cell parameters in a stem cell or population of stem cellsthat may be assessed by time-lapse imaging include, without limitation,the duration of cytokinesis events, time between cytokinesis events,size and shape of the stem cells prior to and during cytokinesis events,number of daughter cells produced by a cytokinesis event, spatialorientation of the cleavage furrow, the rate and/or number of asymmetricdivisions observed (i.e. where one daughter cell maintains a stem cellwhile the other differentiates), the rate and/or number of symmetricdivisions observed (i.e. where both daughter cells either remain as stemcells or both differentiate), and the time interval between theresolution of a cytokinesis event and when a stem cell begins todifferentiate.

Parameters can be measured manually, or they may be measuredautomatically, e.g. by image analysis software. When image analysissoftware is employed, image analysis algorithms may be used that employa probabilistic model estimation technique based on sequential MonteCarlo method, e.g. generating distributions of hypothesizedembryo/pluripotent cell models, simulating images based on a simpleoptical model, and comparing these simulations to the observed imagedata. When such probabilistic model estimations are employed, cells maybe modeled as any appropriate shape, e.g. as collections of ellipses in2D space, collections of ellipsoids in 3D space, and the like. To dealwith occlusions and depth ambiguities, the method can enforcegeometrical constraints that correspond to expected physical behavior.To improve robustness, images can be captured at one or more focalplanes.

Gene Expression Analysis

In some embodiments, the embryos or pluripotent cells are assessed bymeasuring gene expression. In such embodiments, the cell parameter is agene expression level or gene expression profile. Determining theexpression of one or more genes, i.e. obtaining an expression profile orexpression evaluation, may be made by measuring nucleic acidtranscripts, e.g. mRNAs, of the one or more genes of interest, e.g. anucleic acid expression profile; or by measuring levels of one or moredifferent proteins/polypeptides that are expression products of one ormore genes of interest, e.g. a proteomic expression profile. In otherwords, the terms “expression profile” and “expression evaluation” areused broadly to include a gene expression profile at the RNA level orprotein level.

In some embodiments, expression of genes may be evaluated by obtaining anucleic acid expression profile, where the amount or level of one ormore nucleic acids in the sample is determined, e.g., the nucleic acidtranscript of the one or more genes of interest. In these embodiments,the sample that is assayed to generate the expression profile is anucleic acid sample. The nucleic acid sample includes a plurality orpopulation of distinct nucleic acids that includes the expressioninformation of the genes of interest of the embryo or cell beingassessed. The nucleic acid may include RNA or DNA nucleic acids, e.g.,mRNA, cRNA, cDNA etc., so long as the sample retains the expressioninformation of the host cell or tissue from which it is obtained. Thesample may be prepared in a number of different ways, as is known in theart, e.g., by mRNA isolation from a cell, where the isolated mRNA isused as is, amplified, employed to prepare cDNA, cRNA, etc., as is knownin the differential expression art. The sample may be prepared from asingle cell, e.g. a pluripotent cell of a culture of pluripotent cellsof interest, or a single cell (blastomere) from an embryo of interest;or from several cells, e.g. a fraction of a cultures of pluripotentcells, or 2, 3, or 4, or more blastomeres of an embryo of interest,using standard protocols.

The expression profile may be generated from the initial nucleic acidsample using any convenient protocol. While a variety of differentmanners of generating expression profiles are known, such as thoseemployed in the field of differential gene expression analysis, onerepresentative and convenient type of protocol for generating expressionprofiles is array-based gene expression profile generation protocols.Such applications are hybridization assays in which a nucleic acid thatdisplays “probe” nucleic acids for each of the genes to beassayed/profiled in the profile to be generated is employed. In theseassays, a sample of target nucleic acids is first prepared from theinitial nucleic acid sample being assayed, where preparation may includelabeling of the target nucleic acids with a label, e.g., a member ofsignal producing system. Following target nucleic acid samplepreparation, the sample is contacted with the array under hybridizationconditions, whereby complexes are formed between target nucleic acidsthat are complementary to probe sequences attached to the array surface.The presence of hybridized complexes is then detected, eitherqualitatively or quantitatively.

Specific hybridization technology which may be practiced to generate theexpression profiles employed in the subject methods includes thetechnology described in U.S. Pat. Nos. 5,143,854; 5,288,644; 5,324,633;5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464;5,547,839; 5,580,732; 5,661,028; 5,800,992; the disclosures of which areherein incorporated by reference; as well as WO 95/21265; WO 96/31622;WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In these methods,an array of “probe” nucleic acids that includes a probe for each of thephenotype determinative genes whose expression is being assayed iscontacted with target nucleic acids as described above. Contact iscarried out under hybridization conditions, e.g., stringenthybridization conditions, and unbound nucleic acid is then removed. Theterm “stringent assay conditions” as used herein refers to conditionsthat are compatible to produce binding pairs of nucleic acids, e.g.,surface bound and solution phase nucleic acids, of sufficientcomplementarity to provide for the desired level of specificity in theassay while being less compatible to the formation of binding pairsbetween binding members of insufficient complementarity to provide forthe desired specificity. Stringent assay conditions are the summation orcombination (totality) of both hybridization and wash conditions.

The resultant pattern of hybridized nucleic acid provides informationregarding expression for each of the genes that have been probed, wherethe expression information is in terms of whether or not the gene isexpressed and, typically, at what level, where the expression data,i.e., expression profile (e.g., in the form of a transcriptosome), maybe both qualitative and quantitative.

Alternatively, non-array based methods for quantitating the level of oneor more nucleic acids in a sample may be employed, including those basedon amplification protocols, e.g., Polymerase Chain Reaction (PCR)-basedassays, including quantitative PCR, reverse-transcription PCR (RT-PCR),real-time PCR, and the like.

In some embodiments, expression of genes may be evaluated by obtaining aproteomic expression profile, where the amount or level of one or moreproteins/polypeptides in the sample is determined, e.g., theprotein/polypeptide encoded by the gene of interest. In theseembodiments, the sample that is assayed to generate the expressionprofile employed in the methods is a protein sample. Where theexpression profile is proteomic expression profile, i.e. a profile ofone or more protein levels in a sample, any convenient protocol forevaluating protein levels may be employed wherein the level of one ormore proteins in the assayed sample is determined.

While a variety of different manners of assaying for protein levels areknown in the art, one representative and convenient type of protocol forassaying protein levels is ELISA. In ELISA and ELISA-based assays, oneor more antibodies specific for the proteins of interest may beimmobilized onto a selected solid surface, preferably a surfaceexhibiting a protein affinity such as the wells of a polystyrenemicrotiter plate. After washing to remove incompletely adsorbedmaterial, the assay plate wells are coated with a non-specific“blocking” protein that is known to be antigenically neutral with regardto the test sample such as bovine serum albumin (BSA), casein orsolutions of powdered milk. This allows for blocking of non-specificadsorption sites on the immobilizing surface, thereby reducing thebackground caused by non-specific binding of antigen onto the surface.After washing to remove unbound blocking protein, the immobilizingsurface is contacted with the sample to be tested under conditions thatare conducive to immune complex (antigen/antibody) formation. Suchconditions include diluting the sample with diluents such as BSA orbovine gamma globulin (BGG) in phosphate buffered saline (PBS)/Tween orPBS/Triton-X 100, which also tend to assist in the reduction ofnonspecific background, and allowing the sample to incubate for about2-4 hrs at temperatures on the order of about 25°-27° C. (although othertemperatures may be used). Following incubation, the antisera-contactedsurface is washed so as to remove non-immunocomplexed material. Anexemplary washing procedure includes washing with a solution such asPBS/Tween, PBS/Triton-X 100, or borate buffer. The occurrence and amountof immunocomplex formation may then be determined by subjecting thebound immunocomplexes to a second antibody having specificity for thetarget that differs from the first antibody and detecting binding of thesecond antibody. In certain embodiments, the second antibody will havean associated enzyme, e.g. urease, peroxidase, or alkaline phosphatase,which will generate a color precipitate upon incubating with anappropriate chromogenic substrate. For example, a urease orperoxidase-conjugated anti-human IgG may be employed, for a period oftime and under conditions which favor the development of immunocomplexformation (e.g., incubation for 2 hr at room temperature in aPBS-containing solution such as PBS/Tween). After such incubation withthe second antibody and washing to remove unbound material, the amountof label is quantified, for example by incubation with a chromogenicsubstrate such as urea and bromocresol purple in the case of a ureaselabel or 2,2′-azino-di-(3-ethyl-benzthiazoline)-6-sulfonic acid (ABTS)and H₂O₂, in the case of a peroxidase label. Quantitation is thenachieved by measuring the degree of color generation, e.g., using avisible spectrum spectrophotometer.

The preceding format may be altered by first binding the sample to theassay plate. Then, primary antibody is incubated with the assay plate,followed by detecting of bound primary antibody using a labeled secondantibody with specificity for the primary antibody.

The solid substrate upon which the antibody or antibodies areimmobilized can be made of a wide variety of materials and in a widevariety of shapes, e.g., microtiter plate, microbead, dipstick, resinparticle, etc. The substrate may be chosen to maximize signal to noiseratios, to minimize background binding, as well as for ease ofseparation and cost. Washes may be effected in a manner most appropriatefor the substrate being used, for example, by removing a bead ordipstick from a reservoir, emptying or diluting a reservoir such as amicrotiter plate well, or rinsing a bead, particle, chromatograpiccolumn or filter with a wash solution or solvent.

Alternatively, non-ELISA based-methods for measuring the levels of oneor more proteins in a sample may be employed. Representative examplesinclude but are not limited to mass spectrometry, proteomic arrays,xMAP™ microsphere technology, flow cytometry, western blotting, andimmunohistochemistry.

The resultant data provides information regarding expression for each ofthe genes that have been probed, wherein the expression information isin terms of whether or not the gene is expressed and, typically, at whatlevel, and wherein the expression data may be both qualitative andquantitative.

In generating the expression profile, in some embodiments a sample isassayed to generate an expression profile that includes expression datafor at least one gene/protein, sometimes a plurality of genes/proteins,where by plurality is meant at least two different genes/proteins, andoften at least about 3, typically at least about 10 and more usually atleast about 15 different genes/proteins or more, such as 50 or more, or100 or more, etc.

In the broadest sense, the expression evaluation may be qualitative orquantitative. As such, where detection is qualitative, the methodsprovide a reading or evaluation, e.g., assessment, of whether or not thetarget analyte, e.g., nucleic acid or expression product, is present inthe sample being assayed. In yet other embodiments, the methods providea quantitative detection of whether the target analyte is present in thesample being assayed, i.e., an evaluation or assessment of the actualamount or relative abundance of the target analyte, e.g., nucleic acidor protein in the sample being assayed. In such embodiments, thequantitative detection may be absolute or, if the method is a method ofdetecting two or more different analytes, e.g., target nucleic acids orprotein, in a sample, relative. As such, the term “quantifying” whenused in the context of quantifying a target analyte, e.g., nucleicacid(s) or protein(s), in a sample can refer to absolute or to relativequantification. Absolute quantification may be accomplished by inclusionof known concentration(s) of one or more control analytes andreferencing, i.e. normalizing, the detected level of the target analytewith the known control analytes (e.g., through generation of a standardcurve). Alternatively, relative quantification can be accomplished bycomparison of detected levels or amounts between two or more differenttarget analytes to provide a relative quantification of each of the twoor more different analytes, e.g., relative to each other.

Examples of genes whose expression levels are predictive of zygotedevelopmental potential include Cofillin (NM_(—)005507), DIAPH1(NM_(—)001079812, NM_(—)005219), ECT2 (NM_(—)018098), MYLC2/MYL5(NM_(—)002477), DGCR8 (NM_(—)022720), Dicer/DICER1 (NM_(—)030621,NM_(—)177438), TARBP2 (NM_(—)004178, NM_(—)134323, NM_(—)134324), CPEB1(NM_(—)001079533, NM_(—)001079534, NM_(—)001079535, NM_(—)030594),Symplekin/SYMPK (NM_(—)004819), YBX2 (NM_(—)015982), ZAR1(NM_(—)175619), CTNNB1 (NM_(—)001098209, NM_(—)001098210,NM_(—)001098210, NM_(—)001904), DNMT3B (NM_(—)006892, NM_(—)175848,NM_(—)175849, NM_(—)175850), TERT (NM_(—)198253, NM_(—)198255), YY1(NM_(—)003403), IFGR2/IFNGR2 (NM_(—)005534), BTF3 (NM_(—)001037637,NM_(—)001207), and NELF (NM_(—)001130969, NM_(—)001130970,NM_(—)001130971, NM_(—)015537). Other genes whose expression levels mayserve as cell parameters predictive of embryo developmental potentialare provided in FIG. 8. In arriving at a gene expression levelmeasurement, the expression level is often evaluated and then normalizedto a standard control, e.g. the expression level in the sample of a genethat is known to be constant through development, e.g. GAPDH or RPLPO,or of a gene whose expression at that timepoint is known.

Gene expression levels may be determined from a single cell, e.g. ablastomere from an embryo of interest, or an isolated oocyte, or anisolated cell from a culture of stem cells, etc., or they may bedetermined from a embryo, e.g. 2, 3, or 4, or more blastomeres of anembryo of interest, up to and including the whole embryo of interest, ormultiple cells from a culture of stem cells, up to and including thewhole culture of stem cells, etc.

In other aspects, the present invention comprises a protocol forperforming concurrent genotyping and gene expression analysis on asingle cell. For embryos, this can be used to improve pre-implantationgenetic diagnosis (PGD), a procedure where a single cell is removed froman embryo and its DNA is tested for karyotypic defects or the presenceof specific disease genes. Our method allows for concurrent genetic andgene expression analysis. The method involves the following steps: (1)collecting a single cell into a small volume of medium or buffer, (2)performing one-step reverse transcription and polymerase chain reaction(PCR) amplification using a mixture of genotyping and gene expressionanalysis primers, (3) collecting an aliquot of the amplified cDNA afterfewer than 18 cycles of PCR to preserve linearity of the amplification,(4) using the cDNA aliquot to perform gene expression analysis withstandard techniques such as quantitative real-time PCR, (5) using theremaining sample to perform a second round of PCR to further amplify thegenetic information for genotyping purposes, and (6) genotyping usingstandard techniques such as gel electrophoresis.

Determining Developmental Potential from Image and/or Gene ExpressionAnalysis

Once cell parameter measurements have been obtained, the measurementsare employed to determine the developmental potential of theembryo/pluripotent cell. As discussed above, the terms “developmentalpotential” and “developmental competence” refer to the ability orcapacity of a pluripotent cell or tissue to grow or develop. Forexample, in the case of an oocyte or embryo, the developmental potentialmay be the ability or capacity of that oocyte or embryo to grow ordevelop into a healthy blastocyst. As another example, in the case of astem cell, the developmental potential is the ability or capacity togrow or develop into one or more cells of interest, e.g. a neuron, amuscle, a B- or T-cell, and the like. In some embodiments, thedevelopmental potential of an oocyte or embryo is the ability orcapacity of that ooctye or embryo to develop into a healthy blastocyst;to successfully implant into a uterus; to go through gestation; and/orto be born live. In some embodiments, the developmental potential of apluripotent cell is the ability or capacity of that pluripotent cell todevelop into one or more cells of interest, e.g. a neuron, a muscle, aB- or T-cell, and the like; and/or to contribute to a tissue of interestin vivo.

By “good developmental potential’, it is meant that theembryo/pluripotent cell is statistically likely to develop as desired,i.e. it has a 55%, 60%, 70%, 80%, 90%, 95% or more chance, e.g. a 100%chance, of developing as desired. In other words, 55 out of 100, 60 outof 100, 70 out of 100, 80 out of 100, 90 out of 100, 95 out of 100, or100 out of 100 embryos or pluripotent cells demonstrating the cellparameter measurements used to arrive at the determination of gooddevelopmental potential do, in fact, go on to develop as desired.Conversely, by “poor developmental potential’ it is meant that theembryo/pluripotent cell is not statistically likely to develop asdesired, i.e. it has a 50%, 40%, 30%, 20%, 10%, 5% or less chance, e.g.0% chance, of developing as desired. In other words, only 50 out of 100,40 out of 100, 30 out of 100, 20 out of 100, 10 out of 100, or 5 out of100 or less of the embryos or pluripotent cells demonstrating the cellparameter measurements used to arrive at the determination of poordevelopmental potential do, in fact, go on to develop as desired. Asused herein, “normal’ or “healthy’ embryos and pluripotent cellsdemonstrate good developmental potential, whereas “abnormal’ embryos andpluripotent cells display poor developmental potential.

In some embodiments, the cell parameter measurement is used directly todetermine the developmental potential of the embryo/pluripotent cell. Inother words, the absolute value of the measurement itself is sufficientto determine the developmental potential. Examples of this inembodiments using time-lapse imaging to measure cell parameters include,without limitation, the following, any of which alone or in combinationare indicative of good developmental potential in a human embryo: (a) acytokinesis 1 that lasts about 0-30 minutes, for example, about 6-20minutes, on average about 12-14 minutes; (b) a cell cycle 1 that lastsabout 20-27 hours, e.g. about 25-27 hours; (c) a time interval betweenthe resolution of cytokinesis 1 and the onset of cytokinesis 2 that isabout 8-15 hours, e.g. about 9-13 hours, with an average value of about11+/−2.1 hours; (d) a time interval, i.e. synchronicity, between theinitiation of cytokinesis 2 and the initiation of cytokinesis 3 that isabout 0-5 hours, e.g. about 0-3 hours, with an average time of about1+/−1.6 hours. Examples of direct measurements, any of which alone or incombination are indicative of poor developmental potential in a humanembryo, include without limitation: (a) a cytokinesis 1 that lastslonger than about 30 minutes, for example, about 32, 35, 40, 45, 50, 55,or 60 minutes or more; (b) a cell cycle 1 that lasts longer than about27 hours, e.g. 28, 29, or 30 or more hours; (c) a time interval betweenthe resolution of cytokinesis 1 and the onset of cytokinesis 2 that lastmore that 15 hour, e.g. about 16, 17, 18, 19, or 20 or more hours, orless than 8 hours, e.g. about 7, 5, 4, or 3 or fewer hours; (d) a timeinterval between the initiation of cytokinesis 2 and the initiation ofcytokinesis 3 that is 6, 7, 8, 9, or 10 or more hours.

In some embodiments, the cell parameter measurement is employed bycomparing it to a cell parameter measurement from a reference, orcontrol, embryo/pluripotent cell, and using the result of thiscomparison to provide a determination of the developmental potential ofthe embryo/pluripotent cell. The terms “reference” and “control” as usedherein mean a standardized embryo or cell to be used to interpret thecell parameter measurements of a given embryo/pluripotent cell andassign a determination of developmental potential thereto. The referenceor control may be an embryo/pluripotent cell that is known to have adesired phenotype, e.g., good developmental potential, and therefore maybe a positive reference or control embryo/pluripotent cell.Alternatively, the reference/control embryo/pluripotent cell may be anembryo/pluripotent cell known to not have the desired phenotype, andtherefore be a negative reference/control embryo/pluripotent cell.

In certain embodiments, the obtained cell parameter measurement(s) iscompared to a comparable cell parameter measurement(s) from a singlereference/control embryo/pluripotent cell to obtain informationregarding the phenotype of the embryo/cell being assayed. In yet otherembodiments, the obtained cell parameter measurement(s) is compared tothe comparable cell parameter measurement(s) from two or more differentreference/control embryos or pluripotent cells to obtain more in depthinformation regarding the phenotype of the assayed embryo/cell. Forexample, the obtained cell parameter measurements from the embryo(s) orpluripotent cell(s) being assessed may be compared to both a positiveand negative embryo or pluripotent cell to obtain confirmed informationregarding whether the embryo/cell has the phenotype of interest.

As an example, cytokinesis 1 in a normal human embryo, i.e. with gooddevelopmental potential, is about 0-30 minutes, more usually about 6-20minutes, on average about 12-14 minutes, i.e. about 1, 2, 3, 4, or 5minutes, more usually about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, or 20 minutes, in some cases 21, 22, 23, 24, 25, 26, 27, 28, 29,or up to about 30 minutes. A longer period of time to completecytokinesis 1 in the embryo being assessed as compared to that observedfor a normal reference embryo is indicative of poor developmentalpotential. As a second example, cell cycle 1 in a normal embryo, i.e.from the time of fertilization to the completion of cytokinesis 1, istypically completed in about 20-27 hours, more usually in about 25-27hours, i.e. about 15, 16, 17, 18, or 19 hours, more usually about 20,21, 22, 23, or 24 hours, and more usually about 25, 26 or 27 hours. Acell cycle 1 that is longer in the embryo being assessed as compared tothat observed for a normal reference embryo is indicative of poordevelopmental potential. As a third example, the resolution ofcytokinesis 1 and the onset of cytokinesis 2 in normal human embryos isabout 8-15 hours, more often about 9-13 hours, with an average value ofabout 11+/−2.1 hours; i.e. 6, 7, or 8 hours, more usually about 9, 10,11, 12, 13, 14 or up to about 15 hours. A longer or shorter cell cycle 2in the embryo being assessed as compared to that observed for a normalreference embryo is indicative of poor developmental potential. As afourth example, the time interval between the initiation of cytokinesis2 and the initiation of cytokinesis 3, i.e. the synchronicity of thesecond and third mitosis, in normal human embryos is usually about 0-5hours, more usually about 0, 1, 2 or 3 hours, with an average time ofabout 1+/−1.6 hours; a longer interval between the completion ofcytokinesis 2 and cytokinesis 3 in the embryo being assessed as comparedto that observed in a normal reference embryo is indicative of poordevelopmental potential. Finally, as an example of how this embodimentmay be applied when using gene expression levels as parameters forassessing developmental potential, lower expression levels of Cofillin,DIAPH1, ECT2, MYLC2, DGCR8, Dicer, TARBP2, CPEB1, Symplekin, YBX2, ZAR1,CTNNB1, DNMT3B, TERT, YY1, IFGR2, BTF3 and/or NELF, i.e. 1.5-fold,2-fold, 3-fold, 4-fold, 5-fold, 10-fold, 20-fold, 50-fold, or 100-foldlower expression, in 2-cell embryos being assessed as compared to thatobserved for a normal reference 2-cell embryo is indicative of poordevelopmental potential, whereas expression that is equal to or greaterthan that observed for a normal reference 2-cell embryo is indicative ofgood developmental potential. Other examples may be derived fromempirical data, e.g. by observing one or more reference embryos orpluripotent cells alongside the embryo/pluripotent cell to be assessed.Any reference embryo/pluripotent cell may be employed, e.g. a normalreference sample with good developmental potential, or an abnormalreference sample with poor developmental potential. In some cases, morethan one reference sample may be employed, e.g. both a normal referencesample and an abnormal reference sample may be used.

In some embodiments, it may be desirable to use cell parametermeasurements that are arrived at by time-lapse microscopy or byexpression profiling, but not by both time-lapse microscopy andexpression profiling. In other embodiments, it may be desirable to usecell parameter measurements that are arrived at by time-lapse microscopyas well as cell parameter measurements that are arrived at by expressionprofiling.

As discussed above, one or more parameters may be measured and employedto determine the developmental potential of an embryo or pluripotentcell. In some embodiments, a measurement of a single parameter may besufficient to arrive at a determination of developmental potential. Insome embodiments, it may be desirable to employ measurements of morethan one parameter, for example, 2 cell parameters, 3 cell parameters,or 4 or more cell parameters.

In certain embodiments, assaying for multiple parameters may bedesirable as assaying for multiple parameters may provide for greatersensitivity and specificity. By sensitivity it is meant the proportionof actual positives which are correctly identified as being such. Thismay be depicted mathematically as:

${Sensitivity} = \frac{\left( {{Number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {positives}} \right)}{\left( {{{Number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {positives}} + {{Number}\mspace{14mu} {of}\mspace{14mu} {false}\mspace{14mu} {negatives}}} \right)}$

Thus, in a method in which “positives” are the embryos that have gooddevelopmental potential, i.e. that will develop into blastocysts, and“negatives” are the embryos that have poor developmental potential, i.e.that will not develop into blastocysts, a sensitivity of 100% means thatthe test recognizes all embryos that will develop into blastocysts assuch. In some embodiments, the sensitivity of the assay may be about70%, 80%, 90%, 95%, 98% or more, e.g. 100%. By specificity it is meantthe proportion of negatives which are correctly identified as such. Thismay be depicted mathematically as

${Specificity} = \frac{\left( {{Number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {positives}} \right)}{\left( {{{Number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {negatives}} + {{Number}\mspace{14mu} {of}\mspace{14mu} {false}\mspace{14mu} {positives}}} \right)}$

Thus, in a method in which positives are the embryos that have gooddevelopmental potential, i.e. that will develop into blastocysts, andnegatives are the embryos that have poor developmental potential, i.e.that will not develop into blastocysts, a specificity of 100% means thatthe test recognizes all embryos that will not develop into blastocysts,i.e. will arrest prior to the blastocyst stage, as such. In someembodiments, the specificity of the assay may be about 70%, 80%, 90%,95%, 98% or more, e.g. 100%.

As demonstrated in the examples sections below and in FIG. 7, the use ofthree parameters provides sensitivity of 94% and specificity of 93% witha cutoff point of 3 times the standard deviations of the blastocystdistribution. In other words, methods of the invention are able tocorrectly identify the number of embryos that are going to develop intoblastocysts 94% of the time (sensitivity), and the number of embryosthat are going to arrest before the blastocyst stage 93% of the time(specificity). In addition, the specified mean values and/or cut-offpoints may be modified depending upon the data set used to calculatethese values as well as the specific application.

In some embodiments, the assessment of an embryo or pluripotent cellincludes generating a written report that includes the artisan'sassessment of the subject embryo/pluripotent cell, e.g. a “developmentalpotential assessment”, an “assessment of chromosomal abnormalities”,etc. Thus, a subject method may further include a step of generating oroutputting a report providing the results of such an assessment, whichreport can be provided in the form of an electronic medium (e.g., anelectronic display on a computer monitor), or in the form of a tangiblemedium (e.g., a report printed on paper or other tangible medium).

A “report,” as described herein, is an electronic or tangible documentwhich includes report elements that provide information of interestrelating to an assessment arrived at by methods of the invention. Asubject report can be completely or partially electronically generated.A subject report includes at least an assessment of the developmentalpotential of the subject embryo or pluripotent cell, an assessment ofthe probability of the existence of chromosomal abnormalities, etc. Asubject report can further include one or more of: 1) informationregarding the testing facility; 2) service provider information; 3)subject data; 4) sample data; 5) a detailed assessment report section,providing information relating to how the assessment was arrived at,e.g. a) cell parameter measurements taken, b) reference values employed,if any; and 6) other features.

The report may include information about the testing facility, whichinformation is relevant to the hospital, clinic, or laboratory in whichsample gathering and/or data generation was conducted. Sample gatheringcan include how the sample was generated, e.g. how it was harvested froma subject, and/or how it was cultured etc. Data generation can includehow images were acquired or gene expression profiles were analyzed. Thisinformation can include one or more details relating to, for example,the name and location of the testing facility, the identity of the labtechnician who conducted the assay and/or who entered the input data,the date and time the assay was conducted and/or analyzed, the locationwhere the sample and/or result data is stored, the lot number of thereagents (e.g., kit, etc.) used in the assay, and the like. Reportfields with this information can generally be populated usinginformation provided by the user.

The report may include information about the service provider, which maybe located outside the healthcare facility at which the user is located,or within the healthcare facility. Examples of such information caninclude the name and location of the service provider, the name of thereviewer, and where necessary or desired the name of the individual whoconducted sample preparation and/or data generation. Report fields withthis information can generally be populated using data entered by theuser, which can be selected from among pre-scripted selections (e.g.,using a drop-down menu). Other service provider information in thereport can include contact information for technical information aboutthe result and/or about the interpretive report.

The report may include a subject data section, including medical historyof subjects from which oocytes or pluripotent cells were harvested,patient age, in vitro fertilization cycle characteristics (e.g.fertilization rate, day 3 follicle stimulating hormone (FSH) level),and, when oocytes are harvested, zygote/embryo cohort parameters (e.g.total number of embryos). This subject data may be integrated to improveembryo assessment and/or help determine the optimal number of embryos totransfer. The report may also include administrative subject data (thatis, data that are not essential to the assessment of developmentalpotential) such as information to identify the subject (e.g., name,subject date of birth (DOB), gender, mailing and/or residence address,medical record number (MRN), room and/or bed number in a healthcarefacility), insurance information, and the like), the name of thesubject's physician or other health professional who ordered theassessment of developmental potential and, if different from theordering physician, the name of a staff physician who is responsible forthe subject's care (e.g., primary care physician).

The report may include a sample data section, which may provideinformation about the biological sample analyzed in the assessment, suchas the type of sample (embryo or pluripotent cell, and type ofpluripotent cell), how the sample was handled (e.g. storage temperature,preparatory protocols) and the date and time collected. Report fieldswith this information can generally be populated using data entered bythe user, some of which may be provided as pre-scripted selections(e.g., using a drop-down menu).

The report may include an assessment report section, which may includeinformation relating to how the assessments/determinations were arrivedat as described herein. The interpretive report can include, forexample, time-lapse images of the embryo or pluripotent cell beingassessed, and/or gene expression results. The assessment portion of thereport can optionally also include a recommendation(s) section. Forexample, where the results indicate good developmental potential of anembryo, the recommendation can include a recommendation that a limitednumber of embryos be transplanted into the uterus during fertilitytreatment as recommended in the art.

It will also be readily appreciated that the reports can includeadditional elements or modified elements. For example, where electronic,the report can contain hyperlinks which point to internal or externaldatabases which provide more detailed information about selectedelements of the report. For example, the patient data element of thereport can include a hyperlink to an electronic patient record, or asite for accessing such a patient record, which patient record ismaintained in a confidential database. This latter embodiment may be ofinterest in an in-hospital system or in-clinic setting. When inelectronic format, the report is recorded on a suitable physical medium,such as a computer readable medium, e.g., in a computer memory, zipdrive, CD, DVD, etc.

It will be readily appreciated that the report can include all or someof the elements above, with the proviso that the report generallyincludes at least the elements sufficient to provide the analysisrequested by the user (e.g., an assessment of developmental potential).

Utility

As discussed above, methods of the invention may be used to assessembryos or pluripotent cells to determine their developmental potential.This determination of developmental potential may be used to guideclinical decisions and/or actions. For example, in order to increasepregnancy rates, clinicians often transfer multiple embryos intopatients, potentially resulting in multiple pregnancies that pose healthrisks to both the mother and fetuses. Using results obtained from themethods of the invention, the developmental potential of embryos beingtransferred to develop into fetuses is determined prior totransplantation, allowing the practitioner to decide how many embryos totransfer so as to maximize the chance of success of a full termpregnancy while minimizing risk.

Assessments made by following methods of the invention may also find usein ranking embryos or pluripotent cells in a group of embryos orpluripotent cells for their developmental potential. For example, insome instances, multiple embryos may be capable of developing intoblastocysts, i.e. will have good developmental potential. However, someembryos will be more likely to achieve the blastocysts stage or ahigher-quality blastocyst than other, i.e. they will have betterdevelopmental potential than other embryos. In such cases, methods ofthe invention may be used to rank the embryos in the group. In suchmethods, one or more cell parameters for each embryo/pluripotent cell ismeasured to arrive at a cell parameter measurement for eachembryo/pluripotent cell. The one or more cell parameter measurementsfrom each of the embryos or pluripotent cells are then employed todetermine the developmental potential of the embryos or pluripotentcells relative to one another. In some embodiments, the cell parametermeasurements from each of the embryos or pluripotent cells are employedby comparing them directly to one another to determine the developmentalpotential of the embryos or pluripotent cells. In some embodiments, thecell parameter measurements from each of the embryos or pluripotentcells are employed by comparing the cell parameter measurements to acell parameter measurement from a reference embryo/pluripotent cell todetermine the developmental potentials for each embryo/pluripotent cell,and then comparing the determined developmental potentials for eachembryo/pluripotent cell to determine the developmental potential of theembryos or pluripotent cells relative to one another. In this way, apractitioner assessing, for example, multiple zygotes/embryos, canchoose only the best quality embryos, i.e. those with the bestdevelopmental potential, to transfer so as to maximize the chance ofsuccess of a full term pregnancy while minimizing risk.

Assessments made by following the methods of the invention may also finduse in determining the developmental potential of oocytes that arematured in vitro and stem cells that are cultured in vitro. Informationon the developmental potential of oocytes obtained by the methods of theinvention can guide the practitioner's selection of ooctyes tofertilize, resulting in higher probability of success in derivingblastocysts from these oocytes. Likewise, information on thedevelopmental potential of stem cells can inform the practitioner'sselection of stem cells to use in procedures to, e.g. reconstitute orreplace a tissue in vivo in a subject in need thereof.

Reagents, Devices and Kits

Also provided are reagents, devices and kits thereof for practicing oneor more of the above-described methods. The subject reagents, devicesand kits thereof may vary greatly. Reagents and devices of interestinclude those mentioned above with respect to the methods of measuringany of the aforementioned cell parameters, where such reagents mayinclude culture plates, culture media, microscopes, imaging software,imaging analysis software, nucleic acid primers, arrays of nucleic acidprobes, antibodies, signal producing system reagents, etc., depending onthe particular measuring protocol to be performed. For example, reagentsmay include PCR primers that are specific for one or more of the genesCofillin, DIAPH1, ECT2, MYLC2/MYL5, DGCR8, Dicer/DICER1, TARBP2, CPEB1,Symplekin/SYMPK, YBX2, ZAR1, CTNNB1, DNMT3B, TERT, YY1, IFGR2/IFNGR2,BTF3, and NELF, as described above. Other examples of reagents includearrays that comprise probes that are specific for one or more of thegenes of interest, or antibodies to the proteins encoded by these genesof interest.

In addition to the above components, the subject kits will furtherinclude instructions for practicing the subject methods. Theseinstructions may be present in the subject kits in a variety of forms,one or more of which may be present in the kit. One form in which theseinstructions may be present is as printed information on a suitablemedium or substrate, e.g., a piece or pieces of paper on which theinformation is printed, in the packaging of the kit, in a packageinsert, etc. Yet another means would be a computer readable medium,e.g., diskette, CD, etc., on which the information has been recorded.Yet another means that may be present is a website address which may beused via the internet to access the information at a removed site. Anyconvenient means may be present in the kits.

Automated Cell Imaging with a Microscope Array

Some of the methods described above require the ability to observeembryo and stem cell development via time-lapse imaging. This can beachieved using a system comprised of a miniature, multi-channelmicroscope array that can fit inside a standard incubator. This allowsmultiple samples to be imaged quickly and simultaneously without havingto physically move the dishes. One illustrative prototype, shown in FIG.22, consists of a 3-channel microscope array with darkfieldillumination, although other types of illumination could be used. By“three channel,” it is meant that there are three independentmicroscopes imaging three distinct culture dishes simultaneously. Astepper motor is used to adjust the focal position for focusing oracquiring 3D image stacks. White-light LEDs are used for illumination,although we have observed that for human embryos, using red ornear-infrared (IR) LEDs can improve the contrast ratio between cellmembranes and the inner portions of the cells. This improved contrastratio can help with both manual and automated image analysis. Inaddition, moving to the infrared region can reduce phototoxicity to thesamples. Images are captured by low-cost, high-resolution webcams, butother types of cameras may be used.

As shown in FIG. 22, each microscope of the prototype system describedabove is used to image a culture dish which may contain anywhere from1-30 embryos. The microscope collects light from a white light LEDconnected to a heat sink to help dissipate any heat generated by theLED, which is very small for brief exposure times. The light passesthrough a conventional dark field patch for stopping direct light,through a condenser lens and onto a specimen labeled “petri dish,” whichis a culture dish holding the embryos being cultured and studied. Theculture dish may have wells that help maintain the order of the embryosand keep them from moving while the dish is being carried to and fromthe incubator. The wells can be spaced close enough together so thatembryos can share the same media drop. The scattered light is thenpassed through a microscope objective, then through an achromat doublet,and onto a CMOS sensor. The CMOS sensor acts as a digital camera and isconnected to a computer for image analysis and tracking as describedabove.

This design is easily scalable to provide significantly more channelsand different illumination techniques, and can be modified toaccommodate fluidic devices for feeding the samples. In addition, thedesign can be integrated with a feedback control system, where cultureconditions such as temperature, CO2 (to control pH), and media areoptimized in real-time based on feedback and from the imaging data. Thissystem was used to acquire time-lapse videos of human embryodevelopment, which has utility in determining embryo viability for invitro fertilization (IVF) procedures. Other applications include stemcell therapy, drug screening, and tissue engineering.

In one embodiment of the device, illumination is provided by a Luxeonwhite light-emitting diode (LED) mounted on an aluminum heat sink andpowered by a BuckPuck current regulated driver. Light from the LED ispassed through a collimating lens. The collimated light then passesthrough a custom laser-machined patch stop, as shown in FIG. 22, andfocused into a hollow cone of light using an aspheric condenser lens.Light that is directly transmitted through the sample is rejected by theobjective, while light that is scattered by the sample is collected. Inone embodiment, Olympus objectives with 20× magnification are used,although smaller magnifications can be used to increase thefield-of-view, or larger magnifications can be used to increaseresolution. The collected light is then passed through an achromatdoublet lens (i.e. tube lens) to reduce the effects of chromatic andspherical aberration. Alternatively, the collected light from theimaging objective can be passed through another objective, pointed inthe opposing direction, that acts as a replacement to the tube lens. Inone configuration, the imaging objective can be a 10× objective, whilethe tube-lens objective can be a 4× objective. The resulting image iscaptured by a CMOS sensor with 2 megapixel resolution (1600×1200pixels). Different types of sensors and resolutions can also be used.

FIG. 23A shows a schematic drawing of the multi-channel microscope arrayhaving 3 identical microscopes. All optical components are mounted inlens tubes. In operation of the array system, Petri dishes are locatedon the acrylic platforms that are mounted on manual 2-axis tilt stages,which allow adjustment of the image plane relative to the optical axis.These stages are fixed to the base of the microscope and do not moveafter the initial alignment. The illumination modules, consisting ofLEDs, collimator lenses, patch stops, and condenser lenses, are mountedon manual xyz stages for position and focusing the illumination light.The imagine modules consisting of the objectives, achromat lenses, andCMOS sensors, are also mounted on the manual xyz states for positioningof the field-of-view and focusing the objectives. All 2 of the imagingmodules are attached to linear slides and supported by a single leveramr, which is actuated using a stepper motor. This allows forcomputer-controlled focusing and automatic capture of image-stacks.Other methods of automatic focusing as well as actuation can be used.

The microscope array was placed inside a standard incubator, as shown inFIG. 23B. The CMOS image sensors are connected via USB connection to asingle hub located inside the incubator, which is routed to an externalPC along with other communication and power lines. All electrical cablesexit the incubator through the center of a rubber stopper sealed withsilicone glue.

The above described microscope array was used to record time-lapseimages of early human embryo development and documented growth fromzygote through blastocyst stages. Four different experiments monitored atotal of 242 embryos. Out of this group, 100 were imaged up to day 5 or6; the others were removed from the imaging stations at various timepoints for gene expression analysis. A screen shot of the image capturesoftware and imaged embryos is shown in FIG. 24. Images were capturedevery 5 minutes with roughly 1 second of low-light exposure per image.The total amount of light received by the samples was equivalent to 24minutes of continuous exposure, similar to the total level experiencedin an IVF clinic during handling. The 1 second duration of lightexposure per image can be reduced. Prior to working with the humanembryos, we performed extensive control experiments with mousepre-implantation embryos to ensure that both the blastocyst formationrate and gene expression patterns were not affected by the imagingprocess.

FIGS. 25 and 26 show selected images from the time-lapse sequences.Images are shown for day 1, day 2.5, day 4, and day 5.5. For thesequence shown in FIG. 25, 3 out of the 9 embryos developed intoblastocysts, and for the sequence shown in FIG. 26, 5 out of the 12embryos develop into blastocysts. Individual embryos were followed overtime, even though their positions in the photographic field shifted asthe embryos underwent a media change at day 3. The use of sequentialmedia is needed to meet the stage-specific requirements of thedeveloping embryos. During media change, the embryos were removed fromthe imaging station for a few minutes and transferred to new petridishes. In order to keep track of each embryo's identity during mediachange, the transfer of samples from one dish to the other wasvideotaped to verify that embryos were not mixed up. This process wasalso used during the collection of samples for gene expression analysis.The issue of tracking embryo identity can be mitigated by using wells tohelp arrange the embryos in a particular order.

Petri Dish with Micro-Wells

When transferring the petri dishes between different stations, theembryos can sometimes move around, thereby making it difficult to keeptrack of embryo identity. This poses a challenge when time-lapse imagingis performed on one station, and the embryos are subsequently moved to asecond station for embryo selection and transfer. One method is toculture embryos in individual petri dishes. However, this requires eachembryo to have its own media drop. In a typical IVF procedure, it isusually desirable to culture all of a patient's embryos on the samepetri dish and in the same media drop. To address this problem, we havedesigned a custom petri dish with micro-wells. This keeps the embryosfrom moving around and maintains their arrangement on the petri dishwhen transferred to and from the incubator or imaging stations. Inaddition, the wells are small enough and spaced closely together suchthat they can share the same media drop and all be viewed simultaneouslyby the same microscope. The bottom surface of each micro-well has anoptical quality finish. FIG. 27A shows a drawing with dimensions for oneembodiment. In this version, there are 25 micro-wells spaced closelytogether within a 1.7×1.7 mm field-of-view. FIG. 27B shows a 3D-view ofthe micro-wells, which are recessed approximately 100 microns into thedish surface. Fiducial markers, including letters, numbers, and othermarkings, are included on the dish to help with identification.

All references cited herein are incorporated by reference in theirentireties.

EXAMPLES

The following examples are put forth so as to provide those of ordinaryskill in the art with a disclosure and description of how to make anduse the present invention, and are not intended to limit the scope ofwhat the inventors regard as their invention nor are they intended torepresent that the experiments below are all or the only experimentsperformed. Efforts have been made to ensure accuracy with respect tonumbers used (e.g. amounts, temperature, etc.) but some experimentalerrors and deviations should be accounted for. Unless indicatedotherwise, parts are parts by weight, molecular weight is weight averagemolecular weight, temperature is in degrees Centigrade, and pressure isat or near atmospheric.

Sample Source

All embryos used in this study were collected over a multi-year periodand fertilized and cryopreserved by multiple embryologists. The averagenumber of embryos per patient in our study was 3, and all age groupsencountered in a routine IVF center were included. Notably, all of theembryos used for these experiments were IVF-generated (as opposed toICSI), so the embryos were derived from sperm that had relatively normalfunction (at least in terms of their ability to penetrate the cumulus,zona, and oolemma and form a pronuclei). Stimulation protocols werestandard long lupron protocols (cdc.gov/art). Cryopreservation ofsupernumerary human embryos was accomplished by placing them in freezingmedium (1.5 M 1,2propanediol+0.2 M sucrose) for 25 minutes at roomtemperature (22+2° C.). The embryos were then frozen using a slow-freezeprotocol (−1° C./min to −6.5° C.; hold for 5 min; seed; hold for 5 min;−0.5° C./min to −80° C.; plunge in liquid nitrogen). Committee. Noprotected health information could be associated with the embryos.

A large set of cryopreserved embryos were validated and the followingobservations were made: 1) The embryos demonstrated timing indicative ofnormal embryo development in terms of landmarks including: Cleavage to 2cells (occurred early Day 2), onset of RNA degradation (occurred on Days1 to 3), cleavage to 4 and 8 cells (occurred on late Day 2 and Day 3,respectively), activation of the embryonic genome (on Day 3 at the8-cell stage), and formation of the morula and blastocyst (occurred onDays 4 and 5, respectively). 2) The embryos demonstrated an efficiencyin reaching blastocyst stage that is typical of embryos obtained in aclinical setting. This is likely due to the fact that the embryos werecryopreserved at the 2PN stage and represented the array of embryosencountered in an IVF clinic since no “triage” of those that would andwould not develop was done prior to cryopreservation at the 1-cell stage(as is typical of embryos cryopreserved later in development at the Day3 or blastocyst stages). Thus, our data confirms that these embryosexhibited similar blastocyst formation rates compared to those observedin typical IVF clinics. 3) Previous studies have demonstrated thatembryos that are frozen at the 2PN stage exhibit a similar potential fordevelopment, implantation, clinical pregnancy, and delivery whencompared to fresh embryos. Other studies have also shown similar resultsfor frozen oocytes suggesting that the earliest events of human embryodevelopment maintain an appropriate timeline post-cryopreservation. 4)We focused on parameters that were not dependent on time offertilization or thaw time. The first parameter that we measure(duration of the first cytokinesis) is of short duration (ca 10-15 min)and is not dependent on the time of fertilization in this study (it isable to be measured independently in all embryos regardless of finaloutcome). Moreover, all subsequent parameters are measured relative tothis initial measurement point and compared between embryos that succeedto develop to blastocyst and those that fail to do so. 5) Finally, wenote that fresh (unfrozen) embryos that are 3PN are known to developalong the same time frame as fresh normal embryos; we comparedparameters in fresh 3PN embryos that we obtained from the Stanford IVFclinic, and observed that they were not different from those of ourcryopreserved embryos or published reports.

Experimental Plan

In four experimental sets, we tracked the development of 242 pronuclearstage embryos (61, 80, 64 and 37, respectively). In each set ofexperiments, human zygotes were thawed on Day 1 and cultured in smallgroups on multiple plates. Each plate was observed independently withtime-lapse microscopy under darkfield illumination on separate imagingstations. At approximately 24 hour intervals, one plate of embryos wasremoved from the imaging system and collected as either single embryosor single cells (blastomeres) for high throughput real-time quantitativePCR gene expression analysis. Each plate typically contained a mixtureof embryos that reached the expected developmental stage at the time ofharvest (termed “normal”) and those that were arrested or delayed atearlier development stages, or fragmented extensively (termed“abnormal”). Embryos were analyzed as either single intact embryos orwere disassociated into single blastomeres followed by gene-specific RNAamplification. A subset of embryos (100 out of 242) was imaged until Day5 or 6 in order to monitor blastocyst formation.

Human Embryo Culture and Microscopy

Human embryos were thawed by removing the cryovials from the liquidnitrogen storage tank and placing them at room temp. Once a vial wasthawed, it was opened and the embryos were visualized under a dissectingmicroscope. The contents of the vial were then poured into the bottom ofa 3003 culture dish. The embryos were located in the drop and thesurvival of each embryo was assessed and recorded. At room temperature,the embryos were transferred to a 3037 culture dish containing 1.0 M 1,2propanediol+0.2M sucrose for 5 minutes, then 0.5 M 1,2 propanediol+0.2Msucrose for 5 minutes, and 0.0 M 1,2 propanediol+0.2M sucrose for 5minutes. Subsequently, embryos were cultured in Quinn's AdvantageCleavage Medium (CooperSurgical) supplemented with 10% Quinn's AdvantageSerum Protein Substitute (SPS; CooperSurgical) between Day 1 to 3, andQuinn's Advantage Blastocyst Medium (CooperSurgical) with 10% SPS afterDay 3 using microdrops under oil. All of the experiments used the sametype of cleavage-stage medium, except for two stations during the firstexperiment, which used a Global medium (LifeGlobal, Guilford, Conn.). Inthis small subset (12 embryos), the embryos exhibited a slightly lowerblastocyst formation rate (3 out of 12, or 25%) but the sensitivity andspecificity of our predictive parameters were both 100% for this group.

Time-lapse imaging was performed on multiple systems to accommodateconcurrent analysis of multiple samples as well as to validate theconsistency of the data across different platforms. The systemsconsisted of 7 individual microscopes: (1) two modified Olympus IX-70/71microscopes equipped with Tokai Hit heated stages, white-light LuxeonLEDs, and an aperture for darkfield illumination; (2) two modifiedOlympus CKX-40/41 microscopes equipped with heated stages, white-lightLuxeon LEDs, and Hoffman Modulation Contrast illumination (note: thesesystems were used only during the first of 4 experiments after it wasdecided that darkfield illumination was preferable for measuring theparameters); and (3) a custom built 3-channel miniature microscope arraythat fits inside a standard incubator, equipped with white-light LuxeonLEDs and apertures for darkfield illumination. We observed nosignificant difference in developmental behaviour, blastocyst formationrate, or gene expression profiles between embryos cultured on thesedifferent systems; indeed, our parameters for blastocyst prediction wereconsistent across multiple systems and experiments.

The light intensity for all systems was significantly lower than thelight typically used on an assisted reproduction microscope due to thelow-power of the LEDs (relative to a typical 100W Halogen bulb) and highsensitivity of the camera sensors. Using an optical power meter, wedetermined that the power of a typical assisted reproduction microscope(Olympus IX-71 Hoffman Modulation Contrast) at a wavelength of 473 nmranges from roughly 7 to 10 mW depending on the magnification, while thepower of our imaging systems were measured to be between 0.2 and 0.3 mWat the same wavelength. Images were captured at a 1 second exposure timeevery 5 minutes for up to 5 or 6 days, resulting in approximately 24minutes of continuous light exposure. At a power of 0.3 mW, this isequivalent to roughly 1 minute of exposure under a typical assistedreproduction microscope.

To track the identity of each embryo during correlated imaging and geneexpression experiment, we installed a video camera on thestereomicroscope and recorded the process of sample transfer duringmedia change and sample collection. We performed control experimentswith mouse preimplantation embryos (n=56) and a small subset of humanembryos (n=22), and observed no significant difference (p=0.96) in theblastocyst formation rate between imaged and control embryos.

High Throughput qRT-PCR Analysis

For single embryo or single blastomere qRT-PCR analysis, embryos werefirst treated with Acid Tyrode's solution to remove the zona pellucida.To collect single blastomeres, the embryos were incubated in Quinn'sAdvantage Ca²⁺ Mg²⁺ free medium with HEPES (CooperSurgical) for 5 to 20minutes at 37° C. with rigorous pipetting. Samples were collecteddirectly into 10 μl of reaction buffer; subsequent one-step reversetranscription/pre-amplification reaction was performed as previouslydescribed. Pooled 20× ABI assay-on-demand qRT-PCR primer and probe mix(Applied Biosystems) were used as gene-specific primers during thereverse transcription and pre-amplification reactions. High throughputqRT-PCR reactions were performed with Fluidigm Biomark 96.96 DynamicArrays as previously described using the ABI assay-on-demand qRT-PCRprobes. All samples were loaded in 3 or 4 technical replicates. qRT-PCRdata analysis was performed with qBasePlus (Biogazelle), MicrosoftExcel, and a custom built software. Certain genes were omitted from dataanalysis due to either poor data quality (e.g. poor PCR amplificationcurves) or consistent low to no expression in the embryos assessed. Forthe analysis of blastomere age, the maternal transcript panel usedincludes DAZL, GDF3, IFITM1, STELLAR, SYCP3, VASA, GDF9, PDCD5, ZAR1 andZP1, whereas the embryonic gene panel includes ATF7IP, CCNA1, EIF1AX,EIF4A3, H2AFZ, HSP70.1, JARIDIB, LSM3, PABPC1, and SERTAD1. Theexpression value of each gene relative to the reference genes GAPDH andRPLP0, as well as relative to the gene average, was calculated using thegeNorm and ΔΔCt methods. GAPDH and RPLP0 were selected as the referencegenes for this study empirically based on the gene stability value andcoefficient of variation: 1.18 and 46% for GAPDH and 1.18 and 34% forRPLP0. These were the most stable among the 10 housekeeping genes thatwe tested and well within range of a typical heterogeneous sample set.Second, we observed that in single blastomeres, as expected, the amountof RPLP0 and GAPDH transcripts decreased by approximately 1 Ct value perdivision between 1-cell and 8-cell stage, congruent with expectationsthat each cell inherits approximately one half of the pool of mRNA witheach cleavage division, in the absence of new transcripts prior to EGAduring the first 3 days of human development. Third, we noted that theexpression level of these reference genes in single blastomeres remainedstable between 8-cell to morula stage, after EGA began. At the intactembryo level, the Ct values of both RPLP0 and GAPDH remained largelyconstant throughout development until the morula stage with a slightincrease following in the blastocyst stage perhaps due to increasedtranscript levels in the greater numbers of blastomeres present. Most ofthe gene expression analysis performed in this study focused ondevelopmental stages prior to the morula stage, however, when theexpression level of the reference genes was extremely stable.

Automated Cell Tracking

Our cell tracking algorithm uses a probabilistic framework based onsequential Monte Carlo methods, which in the field of computer-vision isoften referred to as the particle filter. The particle filter tracks thepropagation of three main variables over time: the state, the control,and the measurement. The state variable is a model of an embryo and isrepresented as a collection of ellipses. The control variable is aninput that transforms the state variable and consists of our cellpropagation and division model. The measurement variable is anobservation of the state and consists of our images acquired by thetime-lapse microscope. Our estimate of the current state at each timestep is represented with a posterior probability distribution, which isapproximated by a set of weighted samples called particles. We use theterms particles and embryo models interchangeably, where a particle isone hypothesis of an embryo model at a given time. After initialization,the particle filter repeatedly applies three steps: prediction,measurement, and update.

Prediction: Cells are represented as ellipses in 2D space, and each cellhas an orientation and overlap index. The overlap index specifies therelative height of the cells. In general, there are two types ofbehaviour that we want to predict: cell motion and cell division. Forcell motion, our control input takes a particle and randomly perturbseach parameter for each cell, including position, orientation, andlength of major and minor axes. The perturbation is randomly sampledfrom a normal distribution with relatively small variance (5% of theinitialized values). For cell division, we use the following approach.At a given point in time, for each particle, we assign a 50% probabilitythat one of the cells will divide. This value was chosen empirically,and spans a wide range of possible cell divisions while maintaining goodcoverage of the current configuration. If a division is predicted, thenthe dividing cell is chosen randomly. When a cell is chosen to divide,we apply a symmetric division along the major axis of the ellipse,producing two daughter cells of equal size and shape. We then randomlyperturb each value for the daughter cells. Finally, we randomly selectthe overlap indices of the two daughter cells while maintaining theircollective overlap relative to the rest of the cells.

After applying the control input, we convert each particle into asimulated image. This is achieved by projecting the elliptical shape ofeach cell onto the simulated image using the overlap index. Thecorresponding pixel values are set to a binary value of 1 and dilated tocreate a membrane thickness comparable to the observed image data. Sincethe embryos are partially transparent and out-of-focus light iscollected, cell membranes at the bottom of the embryo are only sometimesvisible. Accordingly, occluded cell membranes are added with 10%probability. In practice, we have found that these occluded membranepoints are crucial for accurate shape modeling, but it is important tomake them sparse enough so that they do not resemble a visible edge.

Measurement: Once we have generated a distribution of hypothesizedmodels, the corresponding simulated images are compared to the actualmicroscope image. The microscope image is pre-processed to create abinary image of cell membranes using a principle curvature-based methodfollowed by thresholding. The accuracy of the comparison is evaluatedusing a symmetric truncated chamfer distance, which is then used toassign a weight, or likelihood, to each particle.

Update: After weights are assigned, particles are selected in proportionto these weights to create a new set of particles for the nextiteration. This focuses the particle distribution in the region ofhighest probability. Particles with low probability are discarded, whileparticles with high probability are multiplied. Particle re-sampling isperformed using the low variance method.

Once the embryos have been modeled, we can extract the dynamic imagingparameters such as duration of cytokinesis and time between mitosis, asdiscussed in the main text. Our cell tracking software was previouslyimplemented in Matlab, and computation times ranged from a coupleseconds to half a minute for each image depending on the number ofparticles. Our current version of the software is implemented in C, andcomputation times range from 1 to 5 seconds depending on the number ofparticles.

Example 1 Imaging Analysis to Determine Developmental Potential ofEmbryos Methods

Frozen 1-cell human embryos, also referred to as zygotes, were thawedand placed into culture and cultured under conditions such as those usedin IVF procedures. As described in more detail above, these embryosappear to be representative of the typical in vitro fertilization (IVF)population as they were frozen at the 2PN stage and thusindiscriminately cryopreserved. This is in contrast to embryos typicallycryopreserved at later stages of development following transfer of thoseperceived to be of the highest quality during fresh cycles. For someexperiments, embryos were placed in a standard culture dish. For otherexperiments, embryos were cultured in custom culture dish with opticalquality micro-wells.

The growing embryos, typically between 1 to 30 per dish, were followedindividually by time lapse imaging with a computer controlled microscopeequipped for digital image storage and analysis. In some instances,time-lapse imaging was performed with inverted microscopes equipped withheated stages and incubation chambers. In other instances, time-lapseimaging was performed with custom built miniature microscope arrays thatfit inside a conventional incubator, which enabled the concurrentculture of multiple dishes of samples in the same incubator and wasscalable to accommodate multiple channels with no limitations on theminimum time interval between successive image capture. Using multiplemicroscopes also eliminated the need to move the sample, which improvedthe system accuracy and overall system reliability. The imaging systemsused darkfield illumination, which provided enhanced image contrast forsubsequent feature extraction and image analysis, although it was notedthat other illumination would have been sufficient. The individualmicroscopes in the incubator were isolated from one another, providingeach culture dish with its own controlled environment. This alloweddishes to be transferred to and from the imaging stations withoutdisturbing the environment of the other samples.

Time-lapse images were collected for subsequent analysis of cellularmorphology, including measurement of at least one of the followingcellular parameters: the duration of first cytokinesis, the timeinterval between first and second cell division, and the time intervalbetween the second and third cell division. The images shown in thefigures were taken at 1 second exposure time every 5 minutes for up to 5or 6 days. As described in greater detail below, first cytokinesisusually occurs one day after fertilization and lasts between about 14minutes. First and second cell divisions are usually separated by anaverage of about 11 hours. Second and third cell divisions are usuallyseparated by an average of about 1 hour. Thus, imaging was over a periodof time lasting approximately 36 hours (plus or minus several hours)after fertilization.

Results

The developmental timeline of a healthy human preimplantation embryo inculture was documented over a six day period by time lapse imaging (FIG.2). It was observed that a normal human zygote undergoes the firstcleavage division early on Day 2. Subsequently, the embryo cleaves to a4-cell and 8-cell embryo later on Day 2 and Day 3 respectively, beforecompacting into a morula on Day 4. The first morphologically evidentcellular differentiation is observed on Day 5 and 6 during blastocystformation, when the totipotent blastomeres differentiate to eithertrophectoderm cells, which give rise to extraembryonic structures likethe placenta, or inner cell mass, which develops into the fetus in vivoand pluripotent embryonic stem cells in vitro.

We next tracked the development of 242 normally-fertilized embryos infour independent experiment sets and documented the distribution ofnormal and arrested embryos among samples that were cultured to Day 5 or6. Of the 242 embryos, 100 were cultured to Day 5 or 6 and theblastocyst formation rate was observed to be between 33%-53%, similar tothe blastocyst formation rate at a typical IVF clinic (FIG. 3). Theremaining embryos arrested at different stages of development, mostcommonly between 2-cell and 8-cell stage, and were defined as abnormal(FIG. 3). In order to identify quantitative imaging parameters thatpredict success in embryo development to the blastocyst stage, weextracted and analyzed several parameters from timelapse videos,including blastomere size, thickness of the zona pellucida, degree offragmentation, length of the first cell cycles, time intervals betweenthe first few mitoses, and duration of the first cytokinesis. Duringvideo image analysis of both developmentally normal and abnormalembryos, we observed that many arrested embryos underwent aberrantcytokinesis during the first cell division. Normal embryos completedcytokinesis in a narrow time window of 14.3+/−6.0 min from appearance ofthe cleavage furrows to complete separation of the daughter cells, in asmooth and controlled manner. This is shown in FIG. 4 top. In contrast,abnormal embryos commonly showed one of two aberrant cytokinesisphenotypes. In the milder phenotype, the morphology and mechanism ofcytokinesis appeared normal, but the time required to complete theprocess was longer, ranging from a few additional minutes to an hour(FIG. 4). Occasionally, an embryo that underwent a slightly prolongedcytokinesis still developed into a blastocyst. In the more severephenotype, the morphology and mechanism of cytokinesis were perturbed.For example, as shown in the example in the bottom panel of FIG. 4,embryos formed a one-sided cleavage furrow and underwent an unusualseries of membrane ruffling events for several hours before finallyfragmenting into smaller components. Other variations of such behaviourwere also observed. Additionally, abnormal embryos demonstrating thesemore severe phenotypes frequently became fragmented, providing directevidence that embryo fragmentation is likely a by-product of aberrantcytokinesis that subsequently results in abnormal embryo development.

Detailed analysis of the our imaging results indicated that normalembryos followed strict timing in cytokinesis and mitosis during earlydivisions, before embryonic gene activation (EGA) begins, suggestingthat the developmental potential of an embryo is predetermined byinherited maternal programs. In particular, we noted three temporalintervals, or parameters, in the cell cycles of early-stage embryo thatwere strictly regulated: (1) duration of the first cytokinesis, (2) timeinterval between the first and second mitosis, and (3) synchronicity ofthe second and third mitosis. The relationship between these three timeintervals and morphological changes is shown in FIG. 5. For normalembryos, we measured these parameters to be, approximately, 14.3+/−6.0minutes, 11.1+/−2.1 hours, and 1.0+/−1.6 hours, respectively (given hereas mean plus/minus standard deviation).

We also performed imaging on a small set (n=10) of fresh(non-cryopreserved) embryos that were 3PN (triploid) starting at thesingle-cell stage. 3PN embryos have been shown to follow the sametimeline of landmark events as normal fresh embryos through at least thefirst three cell cycles. These embryos were imaged prior to our mainexperiments in order to validate the imaging systems (but for technicalreasons were not followed out to blastocyst). Out of this set of freshembryos, 3 of the embryos followed a similar timeline of events as ourcryopreserved 2PN embryos, with duration of cytokinesis ranging from 15to 30 min, time between first and second mitosis ranging from 9.6 to13.8 hours, and time between second and third mitosis ranging from 0.3to 1.0 hours. However, in 7 of the embryos we observed a uniquecytokinesis phenotype that was characterized by the simultaneousappearance of 3 cleavage furrows, a slightly prolonged cytokinesis, andultimately separation into three daughter cells (FIG. 4). These embryoshad a duration of cytokinesis ranging from 15 to 70 min (characterizedas the time between the initiation of the cleavage furrows untilcomplete separation into 3 daughter cells), time between first andsecond mitosis (3-cell to 4-cell) ranging from 8.7 to 12.7 hours, andtime between second and third mitosis (4-cell to 5-cell) ranging from0.3 to 2.6 hours. This observation, together with the diverse range ofcytokinesis phenotypes displayed by abnormal embryos, suggests that ourcryopreserved embryos are not developmentally delayed by thecryopreservation process and behave similarly to fresh zygotes thatcleave to 2 blastomeres.

Embryos that reached the blastocyst stage could be predicted, withsensitivity and specificity of 94% and 93% respectively, by having afirst cytokinesis of between 0 to 33 min, a time between first andsecond mitosis of between 7.8 to 14.3 hours, and a time between secondand third mitosis of between 0 to 5.8 hours (FIG. 6). Conversely,embryos that exhibited values outside of one or more of these windowswere predicted to arrest. All the normal embryos that successfullydeveloped into a blastocyst exhibited similar values in all threeparameters. In contrast, the abnormal embryos exhibited a highly amountof variability in the lengths of time they took to complete theintervals (FIG. 6). We observed that (1) a longer period of time tocomplete first cytokinesis than normal indicates poor developmentalpotential; (2) a longer or shorter interval between first and secondcell divisions than normal indicates poor developmental potential; and(3) a longer interval between the second and third cell divisions thannormal indicates poor developmental potential. Thus, these parameterswere predictive of the ability of the embryo to proceed to blastocystformation and blastocyst quality.

Finally, we noted that while each parameter was autonomously predictiveof the developmental potential of the embryo, the use of all threeparameters provided sensitivity and specificity that both exceeded 90%,with a cutoff point of 3 times the standard deviations. The receiveroperating characteristic (ROC) curve for these parameters is shown inFIG. 7. The curve in this figure shows the true positive rate(sensitivity) vs. the false positive rate (1-specificity) for variousstandard deviation cutoffs. To arrive at this ROC, the following numberswere used: Number of true positives=34 (correctly predicted to reachblastocyst); number of true negatives=54 (correctly predicted toarrest); number of false positives=4 (incorrectly predicted to reachblastocyst); number of false negatives=2 (incorrectly predicted toarrest).

Discussion

Our analysis indicates that embryos that follow strict timing in mitosisand cytokinesis during the first three cleavage divisions are much morelikely to both develop to blastocyst stage and form a high-qualityblastocyst with an expanded inner cell mass (ICM). The dynamicmorphological parameters can be used to select the optimal embryos fortransfer or cryo-preservation during an IVF procedure. These parameterscan also be used to distinguish between different qualities ofblastocyst, allowing for a ranking of the relative developmentalpotentials of embryos within a group. The standard practice in IVFclinics is to transfer at the 8-cell stage (day-3). Some clinics chooseto culture embryos to the blastocyst stage (day-5), since blastocysttransfer has up to double the implantation rates compared to day-3transfer. However, many clinics avoid prolonged culture due to increasedrisk of epigenetic disorders. The predictive imaging parameters can beused to predict embryo viability by the 4-cell stage (on day-2) andprior to embryonic gene activation. This can allow for the transfer orcryo-preservation of embryos a full day earlier than is typicallypracticed and before the embryos undergo significant changes in theirmolecular programs. This can also allow for the most optimal embryos tobe selected for PGD or other types of analysis.

Example 2

Validation of imaging parameters through gene expression analysis, anduse of gene expression analysis to determine developmental potential.

Methods

Frozen 1-cell human embryos, also referred to as zygotes, were thawedand placed into culture and cultured under conditions such as those usedin IVF procedures. For some experiments, embryos were placed in astandard culture dish. For other experiments, embryos were cultured incustom culture dish with optical quality micro-wells.

Embryos were removed from the culture and imaging system and collectedas either single embryos or single cells (blastomeres) for geneexpression analysis. Each plate typically contained a mixture ofembryos, with some reaching the expected developmental stage at the timeof harvest, and others arresting at earlier developmental stages orfragmenting extensively. Those that reached the expected developmentalstage at the time of harvest were classified as “normal”, whereas thosethat arrested were considered “abnormal. For example, when a plate ofembryos was removed from the imaging station on late Day 2 for samplecollection, any embryo that had reached 4-cell stage and beyond would beidentified as normal, whereas those that failed to reach 4-cell stagewould be labelled as arrested. These arrested embryos were categorizedby the developmental stage at which they became arrested, such that anembryo with only 2 blastomeres on late Day 2 would be analyzed as anarrested 2-cell embryo. Care was taken to exclude embryos thatmorphologically appeared to be dead and porous at the time of samplecollection (e.g. degenerate blastomeres). Only embryos that appearedalive (for both normal and arrested) were used for gene expressionanalysis. However, it is possible that embryos that appeared normalduring the time of collection might ultimately arrest if they wereallowed to grow to a later stage. Gene expression analysis of embryosrepresentative of each of these classes was performed by quantitativeRT-PCR (qRT-PCR). At approximately 24 hour intervals, embryos werecollected from the individual imaging systems for high throughputqRT-PCR gene expression analysis with multiplex reactions of up to 96genes assayed against 96 sample. Gene expression analysis was performedwith the Fluidigm Biomark System, which can carry out up to 9216simultaneous TaqMan assay-based qRT-PCR reactions in nanoliterquantities.

Results

In order to elucidate molecular mechanisms that may underlie themorphological events, we performed correlated gene expression profiling.The expression levels of 96 different genes belonging to differentcategories were assayed per sample, including housekeeping genes, germcell markers, maternal factors, EGA markers, trophoblast markers, innercell mass markers, pluripotency markers, epigenetic regulators,transcription factors, hormone receptors and others (Table 1, in FIG.19). Two slightly different but overlapping sets of genes were assayedin two different experimental sets, providing a unique set of genesdiagnostic of human embryo fate. The unique gene sets were compiled fromdata regarding gene expression in embryos from model organisms or inhuman embryonic stem cells, as well as from our own unpublishedmicroarray data. The expression status of these gene sets in humanpreimplantation embryos is revealed for the first time in this study.

The expression value of each gene relative to the reference genes GAPDHand RPLPO, as well as relative to the gene average, was calculated usingthe geNorm (El-Toukhy T, et al. (2009) Hum Reprod) and AACt (Vanneste E,et al. (2009) Nat Med 15:577-83) methods. The gene stability value andcoefficient of variation was 1.18 and 46% for GAPDH and 1.18 and 34% forRPLPO, most stable among the 10 housekeeping genes we tested and wellwithin range of a typical heterogeneous sample set. In singleblastomeres, as expected, the amount of RPLPO and GAPDH transcriptsdecreased by approximately 1 Ct value per division between 1-cell and8-cell stage, due to the halving effect of cleavage division as well asthe lack of EGA during the first 3 days of human development. Theexpression level of these reference genes in single blastomeres remainedstable between 8-cell to morula stage. At the whole embryo level, the Ctvalues of both RPLPO and GAPDH remained largely constant throughoutdevelopment until the morula stage. The expression level of RPLPO andGAPDH increased significantly in the blastocysts, most likely due to theincreased number of blastomeres present. These variations did not affectthe validity of RPLPO and GAPDH as reference genes. Most of the geneexpression analysis performed in this study focused on developmentalstages before the morula stage, when the expression level of thereference genes was extremely stable.

Differential Gene Expression Between Normal and Abnormal Embryos.

FIG. 8 shows the average expression level of 52 genes from 6 abnormal 1-to 2-cell embryos and 5 normal 1- to 2-cell embryos plotted in a radargraph on a logarithmic scale. Arrested embryos in general showed reducedamount of mRNA compared to normal embryos, with genes that facilitatedcytokinesis, RNA processing and miRNA biogenesis most severely affected.Genes highlighted with an asterisk indicate a statistically significantdifference (p<0.05) between normal and abnormal embryos as determined bythe Mann-Whitney test. These 18 genes are Cofillin, DIAPH1, ECT2, MYLC2,DGCR8, Dicer, TARBP2, CPEB1, Symplekin, YBX2, ZAR1, CTNNB1, DNMT3B,TERT, YY1, IFGR2, BTF3 and NELF. Each gene belongs to a group asindicated in the Figure, namely Cytokinesis: Cofillin, DIAPH1, ECT2 andMYCL2; miRNA biogenesis: DGCR8, Dicer and TARBP2; RNA processing: YBX2;maternal factors: ZAR1; housekeeping: CTNNB1; pluripotency: DNMT3B, TERTand YY1; receptor: IGFR2; and transcription factor: BTF3 and NELF. Inmost cases, expression of these genes was higher in normal 1- and 2-cellembryos than in arrested 1- and 2-cell embryos.

Interestingly, certain gene categories were affected more in abnormalembryos than others. For example, in abnormal embryos, most of thehousekeeping genes, hormone receptors and maternal factors were notappreciably altered in gene expression, whereas many genes involved incytokinesis and miRNA biogenesis showed significantly reducedexpression. Furthermore, among the genes that were affected, some genesshowed a much larger difference between normal and abnormal embryos thanothers. For example, genes involved in the miRNA biogenesis pathway,such as DGCR8, Dicer and TARBP2, exhibited highly reduced expressionlevels in abnormal embryos. Notably, CPEB1 and Symplekin, two of themost severely affected genes, belonged to the same molecular mechanismthat regulates maternal mRNA storage and reactivation by manipulatingthe length of a transcript's poly(A) tail (Bettegowda, A. et al. (2007)Front. Biosci. 12:3713-3726). These data suggest that embryo abnormalitycorrelates with defects in the embryo's mRNA regulation program.

Correlating Cytokinesis with Gene Expression Profiles.

Gene expression analysis was performed with genes that coded for keycytokinesis components. The identity of each embryo was tracked byinstalling a camera on the stereomicroscope and videotaping the processof sample transfer during media change and sample collection. Whenassessing the gene expression profiles of abnormal embryos, we observeda strong correlation between aberrant cytokinesis and lower geneexpression level in key cytokinesis components. Interestingly, the geneexpression profiles of abnormal embryos were as diverse and variable astheir aberrant morphological phenotypes.

It was discovered that cytokinesis gene expression varied as betweennormal 2-cell embryos and abnormal 2-cell embryos (FIG. 9) and asbetween normal and abnormal 4-cell embryos (FIG. 10). FIGS. 9 and 10show relative expressions of genes which are more highly expressed innormal two cell human embryos (FIG. 9) and normal 4 cell embryos (FIG.10), correlated with different cytokinesis phenotypes. As represented inFIG. 9, an arrested 2-cell embryo that showed abnormal membrane rufflingduring the first cytokinesis had significantly reduced expression levelof all cytokinesis regulatory genes tested. Genes showing differences inFIG. 9 are anillin, cofillin, DIAPH1, DIAPH2, DNM2, ECT2, MKLP2, MYCL2and RhoA. The normal expression levels are given in the bars to theright and can be seen to be higher in each gene. In the photographsabove the graphs of FIG. 9, showing abnormal two cell embryos, the scalebar represents 50 μm. FIG. 10 shows results from an arrested 4-cellembryo that underwent aberrant cytokinesis with a one-sided cytokinesisfurrow and extremely prolonged cytokinesis during the first divisionshowed decreased expression in the cytokinesis regulators Anillin andECT2. Scale bar in FIG. 10 also represents 50 μm.

Embryonic stage specific gene expression patterns.

FIG. 11 shows four Embryonic Stage Specific Patterns (ESSPs) that wereidentified during gene expression analysis of 141 normally developedsingle embryos and single blastomeres. The genes which fall into eachone of the four ESSPs are listed in Table 2 (FIG. 20). The plots in FIG.11 were created by grouping genes based on similar expression patternsand averaging their expression values (relative to reference genes).Relative expression level of an ESSP was calculated by averaging theexpression levels of genes with similar expression pattern. Geneexpression levels are plotted against different cell stages, i.e. 1c=onecell; M=morula, B=blastocyst. In FIG. 11, relative expression of genesin each of the four ESSPs is shown as a function of development, from1-cell (1c) to morula and blastocyst. ESSP1 shows maternallyinheritance, ESSP2 shows gene transcription activation, ESSP3 shows latestage activation, and ESSP4 shows persistent transcripts. As indicatedon ESSP2, the typical transfer point in an IVF clinic occurs at day 3,when the embryos are undergoing significant developmental changes due toembryonic gene activation. Time-lapse image data indicates that thedevelopmental potential of an embryo can be identified by the 4-cellstage, thereby allowing earlier transfer of embryos on day 2 and priorto this gene activation. This early transfer is useful for improving thesuccess rate of IVF procedures.

Table 2 (FIG. 20) lists genes that belong to each of the four ESSPsidentified. Relative gene expression level of each gene was calculatedagainst the reference genes (GAPDH and RPLPO) and relative to the geneaverage. The expression pattern of each gene against the embryo'sdevelopmental timeline followed one of the four following ESSPs: ESSPpattern (1) Early-stage: genes that start high, slowly degrade, and turnoff before blastocyst; ESSP pattern (2) Mid-stage: genes that turn onafter 4-cell stage; ESSP pattern (3) Late-stage: genes that turn on atmorula or blastocyst; and ESSP pattern (4) Constant: genes that haverelatively constant expression values.

ESSP1 described the pattern of maternally inherited genes. Thesetranscripts started with a high expression level at the zygote stage andsubsequently declined as the embryos developed into blastocysts. Thehalf-life of these transcripts was approximately 21 hours. Classicalmaternal factors from other model organisms, such as GDF9 and ZAR1, aswell as germ cell (oocyte) specific genes VASA and DAZL fell under thiscategory. ESSP2 included the embryonic activated genes, which were firsttranscribed in the embryos after the 4-cell stage. Some genes in thiscategory appeared to display two waves of activation, the first andsmaller one at the 5- to 6-cell stage, and the second and larger one atthe 8-cell stage. Known EGA genes from other model organisms, such asEIF1AX31 and JARID1 B32, fell into this category. ESSP3 was comprised oflate activated genes that were not expressed until the blastocyst stage,including the trophoblast marker GCM1. ESSP4 contained persistenttranscripts that maintained stable expression relative to the referencegenes throughout development. The half-life of these genes was 193hours, approximately 9-fold longer than ESSP1. This category included amixture of housekeeping genes, transcription factors, epigeneticregulators, hormone receptors and others. These 4 patterns of geneexpression were confirmed in another experiment set using 61 samples ofsingle normal embryos and blastomeres.

Abnormal embryos exhibiting aberrant cytokinetic and mitotic behaviorduring the first divisions, correlated with highly erratic geneexpression profiles, especially in genes involved in embryonic RNAmanagement. Thus, one may combine these methodologies to provide methodswhich may be used to predict pre-implantation embryo viability. Resultssuggest that abnormal embryos begin life with defective programs in RNAprocessing and miRNA biogenesis, causing excessive degradation ofmaternal mRNA. The stochastic nature of such unregulated RNA degradationleads to random destruction of transcripts, causing the wide variety ofaberrant phenotypes observed in abnormal embryos. Decreased level ofmiRNAs cause defects in regulated maternal RNA degradation, leading todevelopmental arrest at different stages.

Individual Blastomere Analysis.

In order to assess when molecular differentiation began in humanpreimplantation embryos, the expression level of CDX2 in singleblastomeres harvested from 17 embryos at different developmental stageswas analyzed. FIG. 12A shows the relative expression level of two genes,CTBBN1 (dark bars) and CDX2 (light bars) as a function of developmentalstage, from 2 cell to blastocyst. As can be seen, CDX2 was expressedsporadically at low levels in some single blastomeres from embryos priorto the 4-cell stage (FIG. 12A). However, from the 6-cell stage onward,every embryo contained at least 1 blastomere that expressed CDX2 at asignificant level. The expression level of the housekeeping gene CTNNB1also shown in FIG. 12A remained constant among blastomeres from the sameembryo, indicating that the heterogeneous expression pattern of CDX2 wasnot a qPCR artefact. Data from an independent experiment demonstratesimilar observations. These results indicate that moleculardifferentiation in human preimplantation embryos might occur as early asimmediately after the 4-cell stage.

Interestingly, inspection of gene expression profiles in singleblastomeres revealed embryos that contained blastomeres with geneexpression signatures corresponding to different developmental ages. Thegene expression profile of any given embryo at any given time equals thesum of maternal mRNA degradation and EGA. A younger blastomere of earlydevelopmental age typically contains a high amount of maternaltranscripts and a low amount of zygotic genes, and the opposite holdstrue for an older blastomere at a more advanced developmental age. Inthis experiment, the material program was defined as the averageexpression values of 10 ESSP1 markers (maternal transcripts), and theembryonic program by the average expression values of 10 ESSP2 markers(embryonic transcripts). The maternal transcript panel used includesDAZL, GDF3, IFITM1, STELLAR, SYCP3, VASA, GDF9, PDCD5, ZAR1 and ZP1,whereas the embryonic gene panel includes ATF7IP, CCNA1, EIF1 AX,EIF4A3, H2AFZ, HSP70.1, JARID1 B, LSM3, PABPC1, and SERTAD1. Among the 6blastomeres successfully collected from this particular 8-cell embryo, 3blastomeres displayed a gene expression signature similar to blastomeresfrom a normal 3-cell embryo sample, whereas the other 3 blastomeres weresimilar to blastomeres from a normal 8-cell embryo sample (FIG. 12B).The most likely explanation of this observation is arrest of asub-population of cells within the embryo. This partial arrest phenotypewas also observed in another 9-cell embryo and 2 morulas among thesamples we tested. The fact that maternal transcript level remained highin the arrested blastomeres, which had spent the same amount of time inculture as their normal sister cells, indicates that degradation ofmaternal RNA is not a spontaneous process that simply occurs throughtime but most likely requires the functioning of specific RNAdegradation mechanisms such as microRNAs (miRNAs). These data alsoprovide further evidence that maternal mRNA degradation is a conserveddevelopmental event during mammalian embryogenesis and is required fornormal embryo development (Bettegowda, A., et al. (2008) Reprod. Fertil.Dev. 20:45-53). In addition, these data suggest that individualblastomeres in an embryo are autonomous and can develop independently ofeach other. Further, these results indicate that one may use the geneexpression level tests described here to test for a level of an mRNA(which is indicative of gene expression level) in a cell to be tested,where the RNA is of a gene known to be part of the maternal program, andthe persistence of such expression level in a later stage of embryonicdevelopment is correlated with a likelihood of abnormal outcome, or partof the embryonic program, where absence over time is indicative of alikelihood of an abnormal outcome. The maternal program genes examinedhere are ZAR1, PDCD5, NLRP5, H5F1, GDF9 and BNC2. Other maternal effectgenes are known and may be used.

Embryonic Gene Activation.

The present methods are at least in part based on findings thatabnormal, developmentally arrested embryos frequently exhibit aberrantcytokinesis and mitotic timing during the first three divisions beforeEGA (embryonic gene activation) occurs. This suggests that the fate ofembryo development is largely determined by maternal inheritance, afinding in remarkable accordance with a mathematical model of humanpreimplantation development performed by Hardy et al. in 200134.Moreover, anomalies of cytokinesis and mitosis strongly correlate withdecreased levels of maternal transcripts in genes that regulate miRNAbiogenesis and maternal mRNA masking, storage and reactivation. miRNAsregulate translation by promoting mRNA degradation in diverse biologicalprocesses, including organism development and differentiation (Blakaj,A. & Lin, H. (2008) J. Biol. Chem. 283:9505-9508; Stefani, G. & Slack,F. J. (2008) Nat. Rev. Mol. Cell Biol. 9:219-230). Increasing evidencefrom model organisms show that miRNAs may be the key regulators ofmaternal transcript degradation in early embryos (Bettegowda, A., et al.(2008) Reprod. Fertil. Dev. 20:45-53). Thus, defects in miRNA biogenesiswill likely lead to abnormal embryo development. On the other hand,failure to properly manage maternal mRNAs may also lead to poorembryogenesis. Mammalian oocytes synthesize a large pool of maternal RNAtranscripts required to support early embryo growth before the mother'sbirth. These transcripts are repressed and stored for a prolonged periodof time, until they are reactivated after fertilization. Defects in thismaternal RNA management program will likely affect the amount andquality of the maternal transcripts and thus jeopardize the chance ofsuccessful development.

Model for Assessing Embryo Viability.

FIG. 13 shows a model for human embryo development based on correlatedimaging and molecular analysis. Shown is the timeline of developmentfrom zygote to blastocyst including critical brief times for predictionof successful development to blastocyst and a diagram of embryodevelopment. Key molecular data, as diagrammed, indicates that humanembryos begin life with a distinct set of oocyte RNAs that are inheritedfrom the mother. This set of RNAs is maintained and packaged properly byspecific RNA management programs in the egg. Following fertilization,degradation of a subset of maternal RNAs specific to the egg (ESSP1;Embryonic Stage Specific Pattern 1) must be degraded as the transitionfrom oocyte to embryo begins. In parallel, other RNAs are ideallypartitioned equally to each blastomere as development continues (ESSP4).The successful degradation and partitioning of RNAs culminates withembryonic genome activation (EGA) and transcription of the genes ofESSP2 in a cell autonomous manner. Notably, during the cleavagedivisions, embryonic blastomeres may arrest or progress independently.The outcome of cell autonomous development in the embryo is thatindividual blastomeres may arrest or progress and as the 8-cell embryoprogresses to morula stage and beyond, blastocyst quality will beimpacted by the number of cells that arrested or progressed beyond 8cells. Imaging data demonstrates that there are critical periods ofdevelopment that predict success or failure: first cytokinesis, thesecond cleavage division and synchronicity of the second and thirdcleavage divisions. These parameters can be measured automatically usingthe cell tracking algorithms and software previously described. Thesystems and methods described can be used to diagnose embryo outcomewith key imaging predictors and can allow for the transfer of fewerembryos earlier in development (prior to EGA).

Example 3 Imaging Oocyte Maturation and Subsequent Embryo DevelopmentResults

One of the major limitations of current IVF procedures is oocyte qualityand availability. For example, current IVF protocols recruit oocytesfrom the small cyclic pool, providing a small number of oocytes (e.g.1-20) for fertilization. Moreover, approximately 20% of oocytesretrieved following hormone stimulation during IVF procedures areclassified as immature, and are typically discarded due to a reducedpotential for embryo development under current culture conditions.

One method to increase the oocyte pool is through in vitro maturation.FIG. 14 shows three stages of development during in vitro maturation,including germinal vesicle, metaphase I, and metaphase II. The germinalvesicle and metaphase I stages are classified as immature oocytes, whilemetaphase II is classified as mature due to the presence of the firstpolar body, which occurs at 24-48 hours after initiating in vitromaturation. FIG. 15 shows embryo development of an oocyte that has beenmatured in vitro.

Another method to increase the oocyte pool is recruit oocytes from theprimary and secondary pool, providing up to several thousands ofoocytes. In this procedure, dormant follicles are recruited from theovary and programmed in vitro to produce oocytes with normal chromosomecomposition, epigenetic status, RNA expression, and morphology. In otheraspects, the oocytes may be derived from pluripotent stem cellsdifferentiated in vitro into germ cells and matured into human oocytes.

As illustrated in FIG. 14, the maturation process of an oocyte in vitrois marked by several cellular changes that may be used to definecellular parameters for measurement and analysis in the methods of thesubject invention. These include, for example, changes in morphology ofthe oocyte membrane, e.g. the rate and extent of separation from thezona pellucida; changes in the morphology of the oocyte nucleus, e.g.the initiation, completion, and rate of germinal vesicle breakdown(GVBD); the rate and direction of movement of granules in the cytoplasmand nucleus; and the movement of and extrusion of the first polar body.

Example 4 Imaging Stem Cell Differentiation Results

Time-lapse image analysis can also be used to assess the viability,developmental potential, and outcome of other types of cells, such asstem cells, induced pluripotent stem cells (iPSCs), and human embryonicstem cells (hESCs). The developmental potential of stem cells can beassessed by using time-lapse image analysis to measure changes inmorphology during cell development and differentiation (FIG. 17). Thedifferentiated cells can then be analyzed and selected for in vivotransplantation or other use. Several parameters of stem cells may beextracted and analyzed from time-lapse image data, such as the durationof cytokinesis, time between mitosis events, cell size and shape, numberof cells, motion of cells, division patterns, differentiation,asymmetric division (where one daughter cell maintains a stem cell whilethe other differentiates), symmetric division (where both daughter cellseither remain as stem cells or both differentiate), and fatespecification (determining precisely when a stem cell differentiates).

The basic formula of stem cell therapy is that undifferentiated stemcells may be cultured in vitro, differentiated to specific cell types,and subsequently transplanted to recipients for regeneration of injuredtissues and/or organs. Time-lapse image analysis can be used as ahigh-throughput non-invasive device to identify stem cells that formnon-tumorigenic differentiated progeny capable of integration intomature tissues. Potential applications include the treatment ofneurological disorders such as Alzheimer's and Parkinson's, vascularsystem disorders and heart diseases, muscular and skeletal disorderssuch as arthritis, autoimmune diseases and cancers, as well as drugdiscovery by evaluating targets and novel therapeutics.

In humans, damaged tissues are generally replaced by continuousrecruitment and differentiation from stem cells in the body. However,the body's ability for regeneration is reduced with aging. One exampleof this is urinary incontinence resulting from sphincter deficiency.Aging is believed to be one of the principal causes of sphincterdeficiency because the number of muscle fibers and nerves densitydiminish with age. In order to treat patients with incontinence, iPSCsmay be derived from fibroblast cultured from vaginal wall tissues inorder to produce differentiated smooth muscle cells. Thesedifferentiated cells can then be transplanted in vivo. Prior totransplantation, time-lapse image analysis can be used to characterizethe iPSCs with respect to pluripotency, differentiation, methylation,and tumorigenicity. Other applications include time-lapse imaging ofiPSCs that are derived from skin cells of patients with Parkinson's anddifferentiated into neurons for transplantation (FIG. 18).

Example 5 Validation of Imaging Parameters Through Automated Analysis

As evidenced by our time-lapse image data, human embryo development is ahighly variable process between embryos within a cohort and embryos canexhibit a wide range of behaviours during cell division. Thus, themanual characterization of certain developmental events such as theduration of highly abnormal cytokinesis (FIG. 4) may be subject tointerpretation. To validate our imaging parameters and the ability tosystematically predict blastocycst formation, we developed an algorithmfor automated tracking of cell divisions up to the 4-cell stage. Ourtracking algorithm employs a probabilistic model estimation techniquebased on sequential Monte Carlo methods. This technique works bygenerating distributions of hypothesized embryo models, simulatingimages based on a simple optical model, and comparing these simulationsto the observed image data (FIG. 21 a).

Embryos were modeled as a collection of ellipses with position,orientation, and overlap index (to represent the relative heights of thecells). With these models, the duration of cytokinesis and time betweenmitosis can be extracted. Cytokinesis is typically defined by the firstappearance of the cytokinesis furrow (where bipolar indentations formalong the cleavage axis) to the complete separation of daughter cells.We simplified the problem by approximating cytokinesis as the durationof cell elongation prior to a 1-cell to 2-cell division. A cell isconsidered elongated if the difference in axes lengths exceeds 15%(chosen empirically). The time between mitosis is straightforward toextract by counting the number of cells in each model.

We tested our algorithm on a set of 14 human embryos (FIG. 21 b) andcompared the automated measurements to manual image analysis (FIG. 21 c,FIG. 21 d). In this data set, 8 of the 14 embryos reached the blastocyststage with good morphology (FIG. 21 e top). The automated measurementswere closely matched to the manual measurements, and all 8 embryos werecorrectly predicted to reach blastocyst. 2 of the 14 embryos reachedblastocyst with poor morphology (poor quality of inner cell mass; FIG.21 e bottom). For these embryos, manual assessment indicated that 1would reach blastocyst and 1 would arrest, while the automatedassessment predicted that both would arrest. Finally, 4 of the 14embryos arrested prior to the blastocyst stage, and were all correctlypredicted to arrest by both methods.

Particle Filter Framework

The particle filter is a model estimation technique based on Monte Carlosimulation. It is used to estimate unknown or “hidden” models bygenerating distributions of hypothesized models and comparing thesemodels to observed data. Its ability to accommodate arbitrary motiondynamics and measurement uncertainties makes it an ideal candidate fortracking cell divisions.

The particle filter tracks the propagation of three main variables overtime: the state x, the control u, and the measurement z. The statevariable x is a model of the embryo we wish to estimate and isrepresented as a collection of ellipses (for 2D) or ellipsoids (for 3D).The control variable u is an input that transforms the state variableand consists of our cell propagation and division model. The measurementvariable z is an observation of the state and consists of our imagesacquired by the time-lapse microscope. These parameters are described ingreater detail in the following sections.

An estimate of the current state x at each time step t is representedwith a posterior probability distribution. This posterior is oftenreferred to as the belief and is defined as the conditional probabilityof the current state x_(t) given all past image measurements z_(1:t) andpast controls u_(1:t).

bel(x _(t))=p(x _(t) |u _(1:t) ,z _(1:t).

The particle filter approximates the posterior with a set of weightedsamples, or particles, denoted as:

x _(t) =x _(t) ^([1]) ,x _(t) ^([2]) , . . . ,x _(t) ^([M]),

where M is the number of particles. The terms particles and embryomodels are used interchangeably herein. Thus, a single particle xt^([m])(where 1<=m<=M) is one hypothesis of the embryo model at time t.

After initialization, the particle filter repeatedly applies threesteps. The first step is prediction, where each particle is propagatedusing the control input:

x _(t) ^([m]) ˜p(x _(t) |u _(t) ,x _(t-1) ^([m]).

The resulting set of particles is an approximation of the priorprobability. The second step is measurement update, where each particleis assigned an importance weight corresponding to the probability of thecurrent measurement:

w _(t) ^([m]) =p(z _(t) |x _(t) ^([m])).

The set of weighted particles is an approximation of the posteriorbel(xt).

A key component of the particle filter comes in the third step, wherethe set of particles is re-sampled according to their weights. Thisre-sampling step focuses the particle distribution in the region ofhighest probability.

Cell Representation

Cells are represented as ellipses in 2D space. Each cell has a majoraxis, minor axis, and 2-dimensional position in Cartesian coordinates,given by the equation:

${\frac{\left( {x - x_{0}} \right)^{2}}{a^{2}} + \frac{\left( {y - y_{0}} \right)^{2}}{b^{2}}} = 1.$

Each ellipse also has a heading direction θ (yaw), which allows it torotate in the x-y plane. Since ellipses almost always overlap with oneanother, we also denote an overlap index h, which specifies the order ofoverlap (or the relative height of the cells). The parameters for eachembryo model at time t are therefore given as:

${x_{t}^{\lbrack m\rbrack} = \begin{bmatrix}x_{0_{1}} & y_{0_{1}} & a_{1} & b_{1} & \theta_{1} & h_{1} \\x_{0_{2}} & y_{0_{2}} & a_{2} & b_{2} & \theta_{2} & h_{2} \\\vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\x_{0_{N}} & y_{0_{N}} & a_{N} & b_{N} & \theta_{N} & h_{N}\end{bmatrix}},$

where N is the number of cells in that model.

Cell Perturbation and Division

The first step of the particle filter is prediction, where each particleis propagated using the control input. For our application, there aretwo types of behavior that we want to model. The first type of behaviorincludes cell motion, which includes translation, rotation about the yawangle, and changes in length of the major and minor axes. The secondtype of behavior is cell division, where a cell splits into two newcells.

To model cell motion, our control input takes a particle and randomlyperturbs each value for each cell: x_(0i), y_(0i), a_(i), b_(i), θ_(i).The perturbation is randomly sampled from a normal distribution withrelatively small variance (typically set to 5% of the initializedvalues).

To model cell division, we use the following approach. At a given pointin time, for each particle, we assign a 50% probability that one of thecells will divide. This value was chosen empirically, and spans a widerange of possible cell divisions while maintaining good coverage of thecurrent configuration. If a division is predicted, then the dividingcell is chosen randomly. A more sophisticated model could take intoaccount additional factors such as the number of cells in a particle andthe history of their division patterns, and could potentially createmodels based on observed behavior from real data.

When a cell is chosen to divide, a symmetric division along the majoraxis of the ellipse, producing two daughter cells of equal size andshape is applied. Each value for the daughter cells is then randomlyperturbed. The perturbation is again sampled from a normal distributionbut with a larger variance (10% of the initialized values) toaccommodate large variability in the new cell shapes. Finally, theoverlap indices of the two daughter cells are randomly selected whilemaintaining their collective overlap relative to the rest of the cells.

Image Simulation

After applying the control input to each particle, the particlerepresentation must be converted into a simulated image that can becompared to the real images. Accurate image simulation can be adifficult task, and often requires the use of ray-tracing techniques andoptical models. Rather than attempt to simulate realistic images, themethod of the present invention focuses on simulating features that areeasily identifiable in the images. Specifically, images of cellmembranes are simulated.

There are two physical observations that must be taken into account.First, although the microscope is focused on a single plane through theembryo, the depth of field is quite large and out-of-focus light iscollected from almost the entire embryo. And second, the embryos arepartially transparent, which means that the membranes of cells at thebottom of the embryo can sometimes (but not always) be seen through thecells at the top of the embryo.

With these physical observations in mind, there is now described theimage simulation model. For each cell, its corresponding ellipticalshape is projected onto the simulated image using the overlap index h.The corresponding pixel values are set to a binary value of 1 anddilated to create a membrane thickness comparable to the observed imagedata. The overlap index h specifies the order in which cells lie on topof one another. Since occluded cell membranes are only visiblesometimes, if occluded points are detected, they are placed in thesimulated image with low probability (typically around 10%). Inpractice, while these occluded membrane points are necessary foraccurate shape modeling, it is important to make them sparse enough sothat they do not resemble a visible edge.

Image Pre-Processing

The measurement variable z will now be described. A goal of the methodof the present invention is to extract binary images of cell membranesfrom the microscope images for comparison to the simulated images. Thesemembranes exhibit high curvature and high contrast, but are not easilyextracted using intensity or color-based thresholding techniques.Accordingly, a principle curvature-based detector is employed. Thismethod uses the Hessian operator:

${{H\left( {s,\sigma} \right)} = \begin{pmatrix}{I_{xx}\left( {s,\sigma} \right)} & {I_{xy}\left( {s,\sigma} \right)} \\{I_{xy}\left( {s,\sigma} \right)} & {I_{yy}\left( {s,\sigma} \right)}\end{pmatrix}},$

where Ixx, Ixy, and Iyy, are second-order partial derivatives evaluatedat pixel location s and Gaussian scale σ. The eigenvalues of the 2×2Hessian matrix provide information about principle curvatures, while thesign of the eigenvalues distinguish “valleys” from “ridges”43. To detectbright peaks or ridges, the principle curvature at each pixel iscalculated as

p(s)=|min(λ₂,0)|,

where λ2 is the minimum eigenvalue. To detect membranes of varyingthickness, the Hessian operator over a range of scales (i.e.σ<=min<=σ<=σmax) is applied, and the maximum curvature over this rangeis extracted. Finally, the Hessian image is thresholded to create abinary image of the extracted cell membranes. The threshold level istypically set to twice the standard deviation of the pixel values in theHessian.

Particle Weights

As described in the section entitled “Particle Filter Framework,” thesecond main step of the particle filter is measurement update, whereparticles are assigned an importance weight corresponding to theprobability of the current measurement given a particular model. In ourcase, the importance weight is determined by comparing the pre-processedmicroscope image discussed above,” to the simulated image also discussedabove.

This problem has been investigated previously, where particle filterweights were calculated by comparing simulated images to actual imagesusing normalized mutual information. This approach is similar to theidea of occupancy grid matching, which searches for pixel locations thatare either both occupied (value 1) or both empty (value 0). Thesemethods can have trouble when the simulated and actual images aresimilar in shape but slightly misaligned. Instead, the method beingdescribed uses a likelihood function based on the chamfer distance,which measures the average value of the closest distances from one pointset to another. Two sets of points A (in the set of real numbers of sizem), and B (in the set of real numbers of size n), corresponding to thenon-zero pixels in the simulated image and actual image, respectively,are defined. The forward chamfer distance from the point set A to B isgiven as:

${d\left( {A,B} \right)} = {\frac{1}{m}{\sum\limits_{a_{i} \in A}{\min\limits_{b_{j} \in B}{{{a_{i} - b_{j}}}.}}}}$

The backward chamfer distance is defined similarly. The present methodemploys symmetric chamfer distance, which provides a measure of how wellthe simulated image matches the actual image, as well as how well theactual image matches the simulated image:

d _(sym)(A,B)=d(A,B)+d(B,A).

In practice, the individual distance measurements are truncated toreduce the influence of noise. To reduce computation time, distances aredetermined by looking up pixel locations in distance transforms of theimages.

The chamfer distance is used as a likelihood measure of our datameasurement given the estimated model. That is, at time t, for a givenimage measurement z_(t) and a particle model xt_([m]), the particleimportance weight is given as:

w _(t) ^([m])∝exp[−λ·d _(sym)(z _(t) ·x _(t) ^([m]))].

The constant λ is typically set to 1 and can be varied to control the“flatness” of the likelihood distribution.

Particle Re-Sampling and Dynamic Allocation

The third main step of the particle filter is re-sampling, whereparticles are selected in proportion to their weight to create a new setof particles. Particles with low probability are discarded, whileparticles with high probability are multiplied. There has been muchprior work on developing efficient algorithms for re-sampling. Thepresent method uses the low variance approach.

An important issue in particle filters is the choice of the number ofparticles. The simplest choice is to use a fixed value, say M=1000.Then, for each time step, the set of M particles is transformed intoanother set of the same size. In the context of the application, therecan be relatively long periods of time during which the cells areinactive or just slightly changing size and position. Advantage of thisobservation is taken to reduce the processing load by dynamicallyallocating the number of particles according to the amount of cellactivity. That is, when the cells are active and dividing, we increasethe number of particles, and when the cells are inactive, we reduce thenumber of particles.

To measure the degree of cell activity, the sum-of-squared differences(SSD) in pixel intensities between the new image (acquired by themicroscope) and the previous image is calculated. To reduce noise, theimages are first smoothed with a Gaussian filter, and the SSD value issmoothed over time with a causal moving average. The number of particlesis then dynamically adjusted in proportion to this value and truncatedto stay within the bounds 100<M<1000. FIG. 30 is a graph which shows howthe number of particles could be allocated for an embryo dividing fromthe 1-cell to 4-cell stage. It should be noted that this method merelyprovides a measure of the amount of “activity” in the image, but doesnot distinguish between cell division and embryo motion (translationand/or rotation) because a prior image registration was not performed.In this situation (determining the number of particles) this isacceptable since the number of particles should increase in eitherevent. In practice, we also adjust the number of particles based on thenumber of cells in the most likely embryo model. That is, more particlesare generated when more cells are believed to be present in the images.

Limitations of Two-Dimensional Tracking

The 2D cell tracking algorithm described above is useful for determiningthe number of cells in the embryo as well as their 2D shapes. However,it is limited by the fact that there is no underlying physicalrepresentation. This may or may not be important for automaticallytracking cell divisions in order to assess embryo viability. Forexample, certain parameters such as the duration of cytokinesis, and thetime between cell divisions, can be measured using the 2D cell trackingalgorithm. In the next section we extend our 2D model to 3D. To dealwith occlusions and depth ambiguities that arise from estimating 3Dshapes from 2D images, geometric constraints and constraints onconcervation of cell volume are applied.

Cell Representation and Three Dimensional Tracking

This section describes an algorithm for 3D tracking of cell division.Many of the steps from the 2D algorithm carry over into this algorithm,with a few key exceptions. There is a new cell representation for 3Duse. Cells are now represented as ellipsoids in 3D space, given by theequation:

${\frac{\left( {x - x_{0}} \right)^{2}}{a^{2}} + \frac{\left( {y - y_{0}} \right)^{2}}{b^{2}} + \frac{\left( {z - z_{0}} \right)^{2}}{c^{2}}} = 1.$

Each ellipsoid also has a heading direction θ, pitch φ, and roll α.Thus, the representation of each embryo model at time t is given as:

$x_{t}^{\lbrack m\rbrack} = \begin{bmatrix}x_{0_{1}} & y_{0_{1}} & z_{0_{1}} & a_{1} & b_{1} & c_{1} & \theta_{1} & \psi_{1} & \alpha_{1} \\x_{0_{2}} & y_{0_{2}} & z_{0_{2}} & a_{2} & b_{2} & c_{2} & \theta_{2} & \psi_{2} & \alpha_{2} \\\vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\x_{0_{N}} & y_{0_{N}} & z_{0_{N}} & a_{N} & b_{N} & c_{N} & \theta_{N} & \psi_{N} & \alpha_{N}\end{bmatrix}$

One important effect of this revised model is that there can beambiguities associated with inferring 3D shapes from 2D images. Forexample, a cell that is spherical in shape would have a similarappearance to a cell with a longer major axis and larger pitch rotation.This is not a major concern, since as will be shown later on, particledistribution will maintain these multiple hypotheses until enoughinformation is available to make a distinction (e.g., from an event suchas cell division).

Ellipsoids are considered rigid; that is, deformation is not explicitlymodeled. However, we allow a small amount of overlap between neighboringellipsoids, and in these regions of overlap we assume that the cells areflattened against each other. This is an important consideration sinceit is commonly observed in the embryos, and we account for it in thefollowing sections.

Cell Perturbation and Division

Our 3D cell division and perturbation model is similar to the model inSection 4, “Cell Perturbation and Division,” with a few key exceptions.The estimate of 3D shape can be used to enforce conservation of volume.This prevents cells from growing arbitrarily large, particularly in thez-direction. Volume conservation is applied in two situations. First,for cell perturbation, the axes a and b are varied, and c calculatedsuch that volume is conserved for that individual cell. Second, for celldivision, the following constraint is applied:

4/3πa _(p) b _(p) c _(p)=4/3π(a _(d) ₁ b _(d) ₁ c _(d) ₁ +a _(d) ₂ b_(d) ₂ c _(d) ₂ ).

where the subscript p denotes a parent cell and the subscripts d1 and d2denote the two daughter cells. In practice, we allow for a slightviolation of these constraints by letting the total volume of the embryofluctuate between plus/minus 5% of the original volume. This is used tocompensate for potential inaccuracies in the initial volume estimate.

When a cell is chosen to divide in 3D, its division is modeled in thefollowing way. First, for the chosen single cell, a division along thelong axis of the ellipse, which could be either a, b, or c depending onthe configuration, is applied. The daughter cells are initialized to beequal in size and spaced evenly apart, taking into account the rotationof the parent cell. Their parameters are then perturbed to cover a widerange of possible configurations, again using a normal distribution withvariance set to 10% of the initialized values.

Geometric Constraints

The issues of occlusion and depth ambiguity are partially mitigatedthrough conservation of volume. However, constraints regarding thespatial relationships of neighboring ellipsoids are also needed. Thefirst constraint is that cells are prohibited from overlapping by morethan 20% in radius. For cells that overlap by an acceptable amount, theassumption that they have flattened against each other is made. Theparticle model being described represents this phenomenon by ignoringpoints inside intersecting ellipsoids during image simulation. This wasempirically motivated and correlates well with physically observedbehavior.

A second constraint that keeps cells in close proximity is imposed. Thisconstraint is directly related to the physical behavior of humanembryos, where cells are constrained by a membrane called the zonapellucida. The zona is modeled as a spherical shell and use it to imposeboundary conditions. The radius of the zona is set to 30% larger thanthe radius of the 1-cell embryo.

These constraints are enforced as follows. For each particle at a giventime, a random control input is applied to generate a new particle, asdiscussed above. If either of the physical constraints has beenviolated, the new particle is discarded and a new random control isapplied. If a satisfactory new particle is not generated after a certainnumber of attempts, then that particle is discarded.

Image Simulation

The advantage of darkfield illumination, used in the examples, is thatcell membranes scatter light more than the cell interior. This effect ismost pronounced at locations where the cell membranes are parallel tothe optical axis (z-axis). Accordingly, to simulate images theselocations are searched for in our 3D models, which are not necessarilylocated at the equators of the ellipsoids due to their rotation. Thesame rules regarding visible and occluded edges, as discussed above, arethen followed.

Cell Tracking Example in 2D

This example pertains to automated cell microscopy and uses the abovedescribed algorithm for 2D tracking of cell divisions. This model isdesigned to track the number of cells in the image as well as the 2Dcontours of cell membranes. The first step is image acquisition, whichmotivates subsequent sections such as image simulation and imagepre-processing. Time-lapse image sequences for this example wereacquired with a custom Olympus IX-50 inverted microscope with a 10×objective. The microscope is modified for darkfield illumination, wherea hollow cone of light is focused on the sample by placing a circularaperture between the light source and condenser lens. The objective lenscollects light that is scattered by the sample and rejects directlytransmitted light, producing a bright image on a dark background. Anadvantage of darkfield illumination is that cell membranes tend toscatter light more than the cell interior, thereby enhancing theircontrast. The microscope is outfitted with a heated stage and customincubation chamber to allow culturing of the embryos over a period of upto 5 or 6 days. Images were captured at 5-minute intervals by an OlympusSLR digital camera mounted on the side port of the IX-50.

Imaging of embryos began when they were zygotes, or fertilized eggs withroughly spherical shape. To initialize the set of particles, thethresholded Hessian is computed as described in Section 6, “ImagePre-Processing,” and fit a circle to it using least squares. Allparticles are then initialized as circles with random orientationssampled from a uniform distribution.

FIG. 31 shows the results of the 2D algorithm for tracking celldivisions from the 1-cell to 4-cell stage. The results show that cellmembranes are successfully extracted by the algorithm, even for cellstoward the bottom that are partially occluded. It should be noted thatin most particle filter applications, the “single” best model is oftenrepresented as a weighted sum of the state parameters from the particledistribution. However, for the results presented here, the particle withthe highest probability is displayed.

Cell Tracking Example in 3D

FIG. 32 shows two successful applications of the above described 3Dalgorithm for tracking from the 1-cell to 4-cell stage. FIG. 33 is adiagram which shows an example of how particles are distributed during a1-cell to 2-cell division (corresponding to the first example shown inFIG. 32). This plot shows the 3D location of the centers of each cell.As the cell starts to divide, the predictions show an ambiguity in termsof which daughter cell will lie on top of the other, but this isresolved within a couple of frames.

Extracting Predictive Parameters

Once the embryos have been modeled using the methods previouslydescribed, certain parameters can be extracted from the models.Typically, the best or most probable model is used. These parametersinclude, for example, the duration of first cytokinesis, the timebetween the first and second cell divisions, and the time between thesecond and third cell divisions. The duration of cytokinesis can beapproximated by measuring how long a model of a cell is elongated beforeit splits into two cells. Elongation can be measured by looking at theratio of the major to minor axes of the ellipse. Other parameters thatcan be extracted from the models include the time between fertilizationand the first cell division, shapes and symmetries of cells and divisionprocesses, angles of division, fragmentation, etc. Parameters can beextracted using either the 2D cell tracking algorithm or the 3D celltracking algorithm.

Cytokinesis is defined by the first appearance of the cytokinesis furrowto the complete separation of daughter cells. Since our embryo modelsare composed of non-deformable ellipses, identifying the appearance ofthe cytokinesis furrow is a challenging task. One method would be toallow the ellipses to deform, but this results in a more complextracking problem. Another method would be to look for changes incurvature in the pre-processed microscope image; however, this defeatsthe purpose of tying to measure our predictive parameters directly fromthe embryo models. Thus, we simplify the problem by approximating theduration of first cytokinesis as the duration of cell elongation priorto a 1-cell to 2-cell division. Elongation is quantified by calculatingthe ratio of the major-axis a to minor-axis b of the ellipse. A cell isconsidered elongated if:

$\frac{a - b}{b} \geq {15\%}$

This value of 15% was chosen empirically and works well for thisparticular data set; however other values can be used. Once an embryomodel has divided into 2-cells, we can extract the approximated durationof first cytokinesis by calculating the duration of elongation for the1-cell model.

In principle, measuring the time between mitosis events isstraightforward. For example, the time between the first and secondmitosis can be measured as the time between the 2-cell model and the3-cell model. However, in some cases the embryos can exhibit unusual andrandom behavior. This includes, for example, an embryo that goes from1-cell to 2-cell, from 2-cell to an apparent 3- or 4-cell, and then backto 2-cell. The described algorithm is capable of tracking this type ofbehavior, but it poses a challenge for determining the time intervalbetween mitosis events.

One way to deal with this behavior is as follows: Instead of measuringthe time between a 2-cell and 3-cell model (in order to find the timebetween the first and second mitosis), this can be approximated bysimply counting the number of image frames in which a 2-cell model ismost probable. This works well in some cases, but is not alwaysrepresentative of the true time between mitosis events. One can alsodeal with these events by enforcing a restriction on the models based onthe number of cells. That is, when choosing the best or most probablemodel from the distribution at each iteration, one can require that thenumber of cells in the model always stay the same or increase, but neverdecrease. After enforcing this constraint, it is straightforward tocalculate the time between mitosis events. This constraint is alsouseful for filtering tracking results that may show small amounts ofjitter, which can occasionally occur when a model switchesback-and-forth between a 1-cell and 2-cell model, for example.

Method for Extracting Predictive Parameters

FIG. 35 shows a flow chart summarizing the methods described above. Theflow chart shows how a single embryo can be analyzed (although this canbe applied to multiple embryos or other types of cells and stem cells).In the first step, an image of an embryo is acquired with a time-lapsemicroscope (“measurement”). This image can be saved to file andre-opened at a later point in time. The image is usually pre-processedin order to enhance certain features, although this is not necessary.Models of possible embryo configurations are predicted, and images aresimulated from these models (“prediction”). The simulated image couldinclude images of cell membranes, as previously described, or imagesthat more accurately represent the microscope images prior topre-processing. The models are then compared to the pre-processedmicroscope image (“comparison”). Using this comparison, the bestpredictions are kept, while the bad predictions are discarded. Theresulting set of predictions is then used to improve the predictions forthe next image. After performing this process for multiple sequentialimages, it is possible to measure morphological parameters directly fromthe best model(s), such as, for example, the duration of cytokinesis andthe time between mitosis events. These parameters can be used to assessembryo viability, as previously discussed.

Example 7 Automated Analysis of Cell Activity

The methods described above require the ability to track celldevelopment via microscopy. For embryos, it is desirable to trackmultiple embryos, which are being cultured together in the same dish.The analytical methods used here also require that images be takenperiodically (e.g. every 1-30 minutes over 1-5 days for embryos;different time intervals may be used for other types of cells such asstem cells). An imaging method was therefore devised to automaticallytrack embryo development.

In time-lapse microscopy, cells are grown under controlled conditionsand imaged over an extended period of time to monitor processes such asmotility (movement within the environment), proliferation (growth anddivision), and changes in morphology (size and shape). Due to the lengthof experiments and the vast amounts of image data generated, extractingparameters such as the duration of and time between cell divisions canbe a tedious task. This is particularly true for high-throughputapplications where multiple samples are imaged simultaneously. Thus,there is a need for image analysis software that can extract the desiredinformation automatically.

One way to assess embryo viability is to measure the amount of “cellactivity” in the images. This can be achieved simply by takingsequential pairs of images and comparing their pixel values. Morespecifically, to measure the amount of cell activity for each new image,one calculates the sum-of-squared differences (SSD) in pixel intensitiesbetween the new image, denoted as I′, and the previous image, denoted asI′, over all overlapping pixels i:

${SSD} = {{\sum\limits_{i}\left\{ {{I^{\prime}\left( {x_{i}^{\prime},y_{i}^{\prime}} \right)} - {I\left( {x_{i},y_{i}} \right)}} \right\rbrack^{2}} = {\sum\limits_{i}{e_{i}^{2}.}}}$

To reduce noise, the images can first be smoothed with a Gaussianfilter. FIG. 28 shows a plot of the cell activity from day 1 to day 3for a single embryo. As shown, there are sharp peaks corresponding tothe 1-cell to 2-cell division, the 2-cell to 4-cell division, and the4-cell to 8-cell division in a human embryo. The widths of the peaks arerepresentative of the durations of the cell divisions.

One of the limitations of this approach is that the SSD metric onlymeasures the amount of activity in the image, and events such as embryomotion (such as shifting or rotating) can look quite similar to celldivision. One solution to this problem is to perform an imageregistration prior to calculating the SSD. Image registration is theprocess of finding a geometric relationship between two images in orderto align them in the same coordinate system, and can be achieved using avariety of different techniques. For example, one may use a variation ofthe Levenberg-Marquardt iterative nonlinear routine, which registersimages by minimizing the SSD in overlapping pixel intensities. The LMalgorithm transforms pixel locations using a 3×3 homography matrix:

${\begin{bmatrix}{\overset{\sim}{x}}^{\prime} \\{\overset{\sim}{y}}^{\prime} \\{\overset{\sim}{w}}^{\prime}\end{bmatrix} = {\begin{bmatrix}h_{0} & h_{1} & h_{2} \\h_{3} & h_{4} & h_{5} \\h_{6} & h_{7} & 1\end{bmatrix}\begin{bmatrix}x \\y \\1\end{bmatrix}}},$

where the destination pixel locations x′ and y′ are normalized as:

${x^{\prime} = \frac{{\overset{\sim}{x}}^{\prime}}{{\overset{\sim}{w}}^{\prime}}},{y^{\prime} = {{\frac{{\overset{\sim}{y}}^{\prime}}{w^{\prime}}.{Thus}}\text{:}}}$${x^{\prime} = \frac{{h_{0}x} + {h_{1}y} + h_{2}}{{h_{6}x} + {h_{7}y} + h_{8}}},{y^{\prime} = {\frac{{h_{3}x} + {h_{4}y} + h_{5}}{{h_{6}x} + {h_{7}y} + h_{8}}.}}$

The homography matrix can be applied to a variety of imagetransformations, and a reasonable choice in this application would berigid body (Euclidean) transformations. This would align the images ofembryos in translation and in-plane rotation (along the camera axis).However, it is possible to generalize slightly and use an affinetransformation, which allows for image skewing. This generalization mayor may not be desirable depending on the signal trying to be measured.The motion equations thus become:

x′=h ₀ x+h ₁ y+h ₂

y′=h ₃ x+h ₄ y+h ₅.

The LM algorithm first calculates the partial derivatives of e withrespect to the unknown motion parameters h_(k) using the chain rule:

$\frac{\delta \; e}{\delta \; h_{k}} = {{\frac{\delta \; I^{\prime}}{\delta \; x^{\prime}}\frac{\delta \; x^{\prime}}{\delta \; h_{k}}} + {\frac{\delta \; I^{\prime}}{\delta \; y^{\prime}}{\frac{\delta \; y^{\prime}}{\delta \; h_{k}}.}}}$

For the affine motion parameters, these partial derivatives become:

${\frac{\delta \; e}{\delta \; h_{0}} = {x\frac{\delta \; I^{\prime}}{\delta \; x^{\prime}}}},{\frac{\delta \; e}{\delta \; h_{1}} = {y\frac{\delta \; I^{\prime}}{\delta \; x^{\prime}}}},{\frac{\delta \; e}{\delta \; h_{2}} = \frac{\delta \; I^{\prime}}{\delta \; x^{\prime}}},{\frac{\delta \; e}{\delta \; h_{3}} = {x\frac{\delta \; I^{\prime}}{\delta \; y^{\prime}}}},{\frac{\delta \; e}{\delta \; h_{4}} = {y\frac{\delta \; I^{\prime}}{\delta \; y^{\prime}}}},{\frac{\delta \; e}{\delta \; h_{5}} = {\frac{\delta \; I^{\prime}}{\delta \; y^{\prime}}.}}$

Next, using these partial derivatives, the LM algorithm computes theapproximate Hessian matrix A (in the set of real numbers of size 6×6)and weighted gradient vector b (in the set of real numbers of size 6×1)by adding the contribution from each pixel:

${a_{kl} = {\sum\limits_{i}{\frac{\delta \; e_{i}}{\delta \; h_{k}}\frac{\delta \; e_{i}}{\delta \; h_{l}}}}},{b_{k} = {- {\sum\limits_{i}{\frac{\delta \; e_{i}}{\delta \; h_{k}}.}}}}$

Finally, the motion parameters can be updated by adding the incrementalmotion:

ΔH=(A+ΔλI)⁻¹ b,

where the constant λ regulates the step size of the motion update and Iis the identity matrix.

At each iteration of the algorithm, the first image is warped accordingto the updated motion estimate and compared to the second image bycomputing the SSD of pixel intensities in areas of overlap. The presentapplication assumes that the embryo motion between consecutive images isvery small, and therefore only a small, fixed number of iterations areperformed. FIG. 28B shows a plot of cell activity without (28A) and with(28B) image registrations performed for each pair of images. Since theerror function of the Levenberg-Marquardt routine is the SSD, one simplyplots the residual error for each registration. FIG. 29 compares plotsof cell activity for normal and abnormal embryo development. At day 3,the point at which an embryologist would typically evaluate morphology,the embryos look similar and could potentially both be consideredviable. However, their cell activity plots are drastically different, asone of the embryos undergoes a typical series of cell divisions whilethe other splits from a 1-cell embryo into multiple cells and fragments.As expected, the embryo that has a normal activity plot ultimatelyreaches blastocyst by day 5.5.

Other types of image registration may be used prior to calculating theSSD in pixel intensities. This includes, for example, cross correlation,normalized cross correlation, cross phase correlation, mutualinformation, feature detection and tracking, scale invariant featuretransform (SIFT), optical flow, and gradient descent. Imagepre-processing may or may not be desirable prior to registration, suchas feature or contrast enhancement.

Model for Assessing Embryo Viability

FIG. 13 shows a model for human embryo development based on correlatedimaging and molecular analysis. Shown is the timeline of developmentfrom zygote to blastocyst including critical brief times for predictionof successful development to blastocyst and a diagram of embryodevelopment. Key molecular data, as diagrammed, indicates that humanembryos begin life with a distinct set of oocyte RNAs that are inheritedfrom the mother. This set of RNAs is maintained and packaged properly byspecific RNA management programs in the egg. Following fertilization,degradation of a subset of maternal RNAs specific to the egg (ESSP1;Embryonic Stage Specific Pattern 1) must be degraded as the transitionfrom oocyte to embryo begins. In parallel, other RNAs are ideallypartitioned equally to each blastomere as development continues (ESSP4).The successful degradation and partitioning of RNAs culminates withembryonic genome activation (EGA) and transcription of the genes ofESSP2 in a cell autonomous manner. Notably, during the cleavagedivisions, embryonic blastomeres may arrest or progress independently.The outcome of cell autonomous development in the embryo is thatindividual blastomeres may arrest or progress and as the 8-cell embryoprogresses to morula stage and beyond, blastocyst quality will beimpacted by the number of cells that arrested or progressed beyond 8cells. Imaging data demonstrates that there are critical periods ofdevelopment that predict success or failure: first cytokinesis, thesecond cleavage division and synchronicity of the second and thirdcleavage divisions. These parameters can be measured automatically usingthe cell tracking algorithms and software previously described. Thesystems and methods described can be used to diagnose embryo outcomewith key imaging predictors and can allow for the transfer of fewerembryos earlier in development (prior to EGA). Comparison of automatedvs. manual image analysis

FIG. 34 shows a comparison of the automated image analysis to manualimage analysis for a set of 14 embryos. Embryos 1 through 10 (as labeledon the plots) reached the blastocyst stage with varying morphology.Embryos 11 through 14 arrested and did not reach blastocyst. FIG. 34Ashows the comparison for measuring the duration of first cytokinesis,and FIG. 34B shows the comparison for measuring the time between 1st and2nd mitosis. As shown, the two methods show good agreement in general.The small amounts of discrepancy for the duration of first cytokinesisare expected, as they can be attributed to the fact that our automatedanalysis makes an approximation by measuring elongation, as previouslydiscussed. In a few cases, there is a larger disagreement between theautomated and manual analysis for both the duration of cytokinesis aswell as the time between 1st and 2nd mitosis. This occurs for a few ofthe abnormal embryos, and is caused by unusual behavior that is bothdifficult to characterize manually as well as track automatically. Forthis group of embryos, and using just the first two criteria (durationof first cytokinesis and time between 1st and 2nd mitosis), theautomated algorithm has zero false positives. This would be extremelyimportant in an IVF procedure where false positives must be avoided.Manual image analysis had one false negative (embryo 9), while theautomated algorithm had two false negatives (embryos 9 and 10). However,while both embryos 9 and 10 technically reached the blastocyst stage,they showed poor morphology compared to other blastocysts and would beless optimal candidates for transfer. For manual image analysis, embryo14 would be a false positive based on these two criteria, and the thirdparameter of duration between 2nd and 3rd mitosis is needed to give atrue negative. However, the automated algorithm makes the correctprediction using only the first two criteria. These results indicatethat our automated algorithm can successfully predict blastocyst vs.non-blastocyst as well as differentiate between different qualities ofblastocyst. Thus, for situations when multiple embryos are determined tohave good developmental potential, it is possible to calculate a rankingof their relative qualities, in order to select the top 1 or 2 embyrosfor transfer during IVF procedures.

The preceding merely illustrates the principles of the invention. Itwill be appreciated that those skilled in the art will be able to devisevarious arrangements which, although not explicitly described or shownherein, embody the principles of the invention and are included withinits spirit and scope. Furthermore, all examples and conditional languagerecited herein are principally intended to aid the reader inunderstanding the principles of the invention and the conceptscontributed by the inventors to furthering the art, and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the invention as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents and equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure. The scope of the presentinvention, therefore, is not intended to be limited to the exemplaryembodiments shown and described herein. Rather, the scope and spirit ofthe present invention is embodied by the appended claims.

1-37. (canceled)
 38. A method for assessing human embryo developmentalpotential in vitro in an Automated system comprising the steps of: a)capturing periodic sequential images of a human embryo over a 1-5 dayperiod of human embryo development using one or more microscopesconfigured in an incubator that are operably linked to a computercomprising imaging software to capture the sequential images; and (b)using image analysis software configured on the computer to determine acellular activity parameter from the sequential images of step, whereinthe cellular activity parameter comprises the timing interval betweenthe first and second mitosis of the human embryo and the timing intervalbetween the second and third mitosis of the human embryo, and c) usingsoftware configured on the computer to assess the developmentalpotential of the human embryo from the cellular activity parameter ofstep b.
 39. The method of claim 38, wherein the sequential images arecaptured every 1-30 minutes.
 40. The method of claim 38, wherein thesequential images are captured every 5 minutes.
 41. An automated systemfor assessing human embryo developmental potential in vitro comprising:a) an incubator; b) one or more microscopes configured in the incubatoreach comprising a camera capable of capturing images from themicroscope; c) a computer comprising: i) imaging software capable ofcapturing sequential images from the one or more microscopesperiodically over a 1-5 day period of human embryo development; and ii)image analysis software capable of determining a cellular activityparameter from the sequential images of step c) i) and determining thedevelopmental potential of the human embryo from the cellular activityparameter, wherein the cellular activity parameter comprises the timinginterval between the first and second mitosis of the human embryo andthe timing interval between the second and third mitosis of the humanembryo.
 42. The automated system of claim 41, further comprising imageanalysis software configured to compare the pixel values of sequentialpairs of images to determine the amount of cellular activity for themost recent image of the pair by comparing pixel intensities of the mostrecent image to the pixel intensity of the previous image of the pair.43. The automated system of claim 41, wherein the cellular activityparameters are measured annually.
 44. The automated system of claim 41,wherein the cellular activity parameters are measured automatically. 45.The automated system of claim 41, wherein the one or more microscopesemploy darkfield illumination.
 46. The automated system of claim 41,wherein the images are captured every 1-30 minutes.
 47. The automatedsystem of claim 41, wherein the images are captured every 5 minutes. 48.The automated system of claim 41, comprising a single microscope. 49.The automated system of claim 41, comprising an array of microscopes.50. The automated system of claim 41, wherein the image analysissoftware is configured to determine a cellular activity parameterselected from the group consisting of cell size thickness of the zonapellucida, degree of fragmentation, symmetry of daughter cells andduration of at least one cytokinesis.
 51. The automated system of claim41, wherein the image analysis software is configured to determine thetime interval between the observation of a cleavage furrow and theresolution of cleavage furrow into two daughter cells.
 52. The automatedsystem of claim 41, wherein the image analysis software is configured todetermine the duration of a cell cycle event.
 53. The automated systemof claim 41, wherein the image analysis software is configured to assessembryos selected from the group consisting of one cell embryos, two cellembryos, three cell embryos, four cell embryos, 5 cell embryos, and 6cell embryos.
 54. The automated system of claim 41, wherein the imageanalysis software comprises an algorithm for automated tracking of celldivision.
 55. The automated system of claim 41, wherein the imageanalysis software models embryos as a collection of ellipses withposition, orientation and overlap index.
 56. The automated system ofclaim 41, wherein the one or more microscopes track multiple embryos.57. The automated system of claim 41, wherein the timing intervalbetween the first and second mitosis comprises the timing intervalbetween the resolution of cytokinesis 1 and the onset of cytokinesis 2or the resolution of cytokinesis 1 and the resolution of cytokinesis 2.58. The automated system of claim 41, wherein the timing intervalbetween the second and third mitosis comprises the timing intervalbetween the resolution of cytokinesis 2 and the onset of cytokinesis 3or the resolution of cytokinesis 2 and the resolution of cytokinesis 3.59. The automated system of claim 41, wherein the image analysissoftware is further configured to measure the duration of cell cycle 1,cell cycle 2 cell cycle 3, and cell cycle
 4. 60. The automated system ofclaim 41, wherein good development potential is indicated by a timeinterval between the first and second mitosis of 8 to 15 hours.
 61. Theautomated system of claim 41, wherein good developmental potential isindicated by a time interval between the second and third mitosis of 0to 50 hours.
 62. The automated system of claim 41, wherein gooddevelopment potential is indicated by a time interval between the firstand second mitosis of 8 to 15 hours and a time interval between thesecond and third mitosis of 0 to 5 hours.
 63. The automated system ofclaim 59, wherein good developmental potential is indicated by aduration of cell cycle 1, cell cycle 2, cell cycle 3, and cell cycle 4that takes place within 54 hours and results in a 5 cell embryo.
 64. Theautomated system of claim 63, wherein good developmental potential isindicated by a duration of cell cycle 1, cell cycle 2, cell cycle 3, andcell cycle 4 that takes place within 54 hours and results in a 5 cellembryo and a time interval between the first and second mitosis of 8 to15 hours.
 65. The automated system of claim 63, wherein gooddevelopmental potential is indicated by a duration of cell cycle 1, cellcycle 2, cell cycle 3, and cell cycle 4 that takes place within 54 hoursand results in a 5 cell embryo and a time interval between the secondand third mitosis of 0 to 5 hours.
 66. An automated system for assessinghuman embryo development potential in vitro comprising; (iii) anincubator; (iv) one or more microscopes configured in the incubator eachcomprising a camera capable of capturing images from the microscope; (v)a computer configured to assess embryo development parameters whereinthe computer comprises: (i) imaging software capable of capturingsequential pairs of imaged from the one or more microscopes every 1-30minutes over a 1-5 day period of human embryo development; (ii) imageanalysis software capable of determining a cellular activity parametercomprising the timing interval between the first and second mitosis ofthe human embryo and the timing interval between the second and thirdmitosis of the embryo and further capable of determining thedevelopmental potential of the human embryo from the cellular activityparameter, wherein good development potential is indicated by a timeinterval between the first and second mitosis of 8 to 15 hours and/or atime interval between the second and third mitosis of 0 to 5 hours.